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Conference Close

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Sunday Lightning Talks

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Day 2 SciPy Tools Plenary Session

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Day 3 Lightning Talks

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Day 3 SciPy Tools Plenary Session

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Closing Session

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Day 2 Lightning Talks

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Lightning Talks 2019-07-11

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SciPy Tools Plenary Session

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GeoPython 2019 (June 25) Track 1

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GeoPython 2019 (June 26) Track 1

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GeoPython 2019 (June 26) Track 2

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GeoPython 2019 (June 26) Track 2-2

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GeoPython 2019 (June 25) Track 2

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PyCon CZ Ostrava 2019 DAY 2 - BALLROOM - Live Stream

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PyCon CZ Ostrava 2019 DAY 2 - CLUB - Live Stream

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Security Issues on your Python Code - Harley Davidson Karel

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PyCon CZ Ostrava 2019 DAY 1 - BALLROOM - Live Stream

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PyCon CZ Ostrava 2019 DAY 1 - CLUB - Live Stream

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Automated Testing for Prose - Katy Decorah Lightning Talk

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Captioning the Docs - Vanessa Stanton - Behind the Scenes of Write the Docs

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Subtle Issues with camelCase filenames - Carl Parker Lightning Talk

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Harvest Past Experience to be a Great Tech Writer - Mark F Iverson

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How I post conference videos the same day - Behind the Scenes

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Lightning Talk: Comics in Docs - Tess Needham

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Lightning Talk: Documenting the Documentarians - Kay Smoljak

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Lightning Talk: How to host a doc bug bash - Lucie Lozinski

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Lightning Talk: Influencing users to find answers in our docs - Grace Greenwood

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Lightning Talk: To succeed as an internal writer... - Colin Torretta

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Lightning Talk: Why you need peer reviews - Michael Khmelnitsky

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Welcome - Write the Docs Portland 2019

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PyConX Conference Welcome and Introduction

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Intro to SciPy Japan 2019: Scientific Python Conference

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DjangoCon 2019 - Day 3 Lightning Talks

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DjangoCon 2019 - Day 2 Lightning Talks

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DjangoCon 2019 - Day 1 Lightning Talks

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How PyLadies Brazil became the biggest PyLadies chapter of the world

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Djangocon 2019 - Europe - Day 2

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Djangocon 2019 - Europe - Day 1

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А что, если без Python? Julia для машинного обучения и вообще / Глеб Ивашкевич

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Видеоотчет о Moscow Python Conf ++ 2019

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PyCon SK 2019 - Aula Magna - Nedela

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PyCon SK 2019 - Aula Magna - Sobota

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PyCon SK 2019 - Aula Minor - Sobota

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PyCon SK 2019 piatok - Aula Minor (Aula Minor)

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Aftermovie 2019 - Pycon Colombia 2019

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Andres Giraldo Carvajal - Interactive Data Visualization in Python - PyCon Colombia 2019

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Carlos Miguel Patiño - Introduction to Generative Adversarial Networks - PyCon Colombia 2019

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David Delgado Ruiz - Story of an junior engineer - PyCon Colombia 2019

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Diego Camilo Peña - IOT and hardware interaction with Python - PyCon Colombia 2019

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Eduardo Ismael Garcia - Creating a BlockChain with Python - PyCon Colombia 2019

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Interview Benjamin Lewis - Keynote Speaker - PyCon Colombia 2019

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Interview Jake Vanderplass - Keynote Speaker - PyCon Colombia 2019

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Interview Jessica Mckellar - Keynote Speaker - PyCon Colombia 2019

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Javier Daza Comas - Creating succesfull tech communities - Pycon Colombia 2019

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Johana Salinas Quintana - How to become a Data Science Superhero? - PyCon Colombia 2019

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Luis David Camacho - High Perfomance Pandas - PyCon Colombia 2019

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PyCon Colombia 2019 promo

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A developer amongst (data) journalists by Eléonore Mayola

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Binding the Sea by Åke Forslund

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Build your own event loop by Pradip Caulagi

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Closing keynote by Adam Tornhill

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Designing an intuitive framework for complex pipelines in PySpark by Sebastian Ånerud

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Digging MUD in Python by Samuel Regandell

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Gait phase recognition using machine learning algorithm with IMU sensors by Binbin Su

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Mantra - A Deep Learning Development Kit by Ross Taylor

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Opening keynote by Lorena Mesa - Now is better than Never...

Image from PyCon Sweden 2018 - lightning talks part 1

PyCon Sweden 2018 - lightning talks part 1

Image from PyCon Sweden 2018 - lightning talks part 2

PyCon Sweden 2018 - lightning talks part 2

Image from PyCon Sweden 2018 - lightning talks part 3

PyCon Sweden 2018 - lightning talks part 3

Image from PyCon Sweden 2018 opening by chairwoman Anna K. Lindegren

PyCon Sweden 2018 opening by chairwoman Anna K. Lindegren

Image from Charlas Rápidas. 2018-11-24 Sábado (Mañana)

Charlas Rápidas. 2018-11-24 Sábado (Mañana)

Image from Charlas Rápidas. 2018-11-24 Sábado (Tarde)

Charlas Rápidas. 2018-11-24 Sábado (Tarde)

Image from Charlas Rápidas. 2018-11-24 Viernes

Charlas Rápidas. 2018-11-24 Viernes

Image from Deploying Django App On AWS Lambda

Deploying Django App On AWS Lambda

Image from Application of Recurrent Neural Networks to innovative drug design - Rafał A. Bachorz

Application of Recurrent Neural Networks to innovative drug design - Rafał A. Bachorz

Image from Comixify: Turning videos into comics - Adam Svystun, Maciej Pęśko, Tomasz Trzcinski

Comixify: Turning videos into comics - Adam Svystun, Maciej Pęśko, Tomasz Trzcinski

Image from Conference Opening Notes

Conference Opening Notes

Image from Distributed deep learning and why you may not need it - Jakub Sanojca, Mikuláš Zelinka

Distributed deep learning and why you may not need it - Jakub Sanojca, Mikuláš Zelinka

Image from In Browser AI - neural networks for everyone - Kamila Stepniowska, Piotr Migdał

In Browser AI - neural networks for everyone - Kamila Stepniowska, Piotr Migdał

Image from Keynote - Stefania Druga -

Keynote - Stefania Druga - "Cognimates: Read, Write and Tinker with AI"

Image from Learning to rank @ allegro.pl - Tomasz Bartczak, Ireneusz Gawlik

Learning to rank @ allegro.pl - Tomasz Bartczak, Ireneusz Gawlik

Image from Predicting preterm birth with convolutional neural nets - Tomasz Włodarczyk, Szymon Płotka

Predicting preterm birth with convolutional neural nets - Tomasz Włodarczyk, Szymon Płotka

Image from Recognizing products from raw text descriptions using... - Tymoteusz Wołodźko, Tomasz Płomiński

Recognizing products from raw text descriptions using... - Tymoteusz Wołodźko, Tomasz Płomiński

Image from Spammers vs. Data: My everyday fight - Juan De Dios Santos

Spammers vs. Data: My everyday fight - Juan De Dios Santos

Image from The smart shopping basket: A Case Study with deep..- Marcin Stachowiak, Michal Dura, Piotr Szajowski

The smart shopping basket: A Case Study with deep..- Marcin Stachowiak, Michal Dura, Piotr Szajowski

Image from Beautiful, Interactive, and Portable Maps using Folium and Live API Data - Ariel M'ndange-Pfupfu

Beautiful, Interactive, and Portable Maps using Folium and Live API Data - Ariel M'ndange-Pfupfu

Image from Dimensionally reducing data squeezing out the good stuff - Aabir Abubaker Kar

Dimensionally reducing data squeezing out the good stuff - Aabir Abubaker Kar

Image from Graphs: Datastructures to Query - Benjamin Ortiz Ulloa

Graphs: Datastructures to Query - Benjamin Ortiz Ulloa

Image from Keynote: The Business of Open Source - Travis E. Oliphant

Keynote: The Business of Open Source - Travis E. Oliphant

Image from Mining dockless bikeshare and dockless scootershare trip data - Stefanie Brodie, Kiana Roshan Zamir

Mining dockless bikeshare and dockless scootershare trip data - Stefanie Brodie, Kiana Roshan Zamir

Image from Recreating, Understanding, and Visualizing FiveThirtyEight's... - Matthew Brems and Joseph Nelson

Recreating, Understanding, and Visualizing FiveThirtyEight's... - Matthew Brems and Joseph Nelson

Image from Scaling PyData with Dask - Martin Durant and Jim Crist

Scaling PyData with Dask - Martin Durant and Jim Crist

Image from uarray - Efficient and Generic Array Computation - Travis E. Oliphant, Saul Shanabrook

uarray - Efficient and Generic Array Computation - Travis E. Oliphant, Saul Shanabrook

Image from Why do you have to make it so complicated? - James Powell, Brandon Burroughs, Daniel Allan

Why do you have to make it so complicated? - James Powell, Brandon Burroughs, Daniel Allan

Image from PyParis 2018 - Sponsor speech (Wifirst)

PyParis 2018 - Sponsor speech (Wifirst)

Image from [ENG] Alexander Todorov:

[ENG] Alexander Todorov: "How to write pylint plugins" / #PiterPy

Image from [ENG] Аnton Shalimov:

[ENG] Аnton Shalimov: "Aviasales. A look under the hood. Tornado based search engine." / #PiterPy

Image from [ENG] Artem Malyshev:

[ENG] Artem Malyshev: "Readability matters" / #PiterPy

Image from [ENG] Denis Kataev:

[ENG] Denis Kataev: "Python and binary data" / #PiterPy

Image from [ENG] Dmitry Dygalo:

[ENG] Dmitry Dygalo: "Testable code: making the (testing) world better" / #PiterPy

Image from [ENG] Dmitry Karpov:

[ENG] Dmitry Karpov: "Real-time apps with Python. Tips and tricks" / #PiterPy

Image from [ENG] Dmitry Nazarov:

[ENG] Dmitry Nazarov: "EdgeDB workshop" / #PiterPy

Image from [ENG] Jonas Obrist:

[ENG] Jonas Obrist: "Artisanal Async Adventures" / #PiterPy

Image from [ENG] Juan Luis Cano:

[ENG] Juan Luis Cano: "Catch that asteroid with poliastro!" / #PiterPy

Image from [ENG] Konstantin Ignatov:

[ENG] Konstantin Ignatov: "Python application monitoring: let's do it logging style" / #PiterPy

Image from [ENG] Lena Volzhina:

[ENG] Lena Volzhina: "How to combine physical models, ML and production performance" / #PiterPy

Image from [ENG] Lynn Root:

[ENG] Lynn Root: "asyncio in Practice: We Did It Wrong" / #PiterPy

Image from [ENG] Michal Karzynski:

[ENG] Michal Karzynski: "Developing elegant workflows with Apache Airflow" / #PiterPy

Image from [ENG] Miroslav Šedivý:

[ENG] Miroslav Šedivý: "A Day Has Only 24±1 Hours: import pytz" / #PiterPy

Image from [ENG] Panel discussion: The Future of Python / #PiterPy

[ENG] Panel discussion: The Future of Python / #PiterPy

Image from [ENG] Pascal van Kooten:

[ENG] Pascal van Kooten: "High-Frequency Cryptocurrency Trading using Asyncio" / #PiterPy

Image from [ENG] Piotr Migdal:

[ENG] Piotr Migdal: "Learning neural networks within Jupyter Notebook" / #PiterPy

Image from [RUS] Alexander Todorov:

[RUS] Alexander Todorov: "How to write pylint plugins" / #PiterPy

Image from [RUS] Аnton Shalimov:

[RUS] Аnton Shalimov: "Aviasales. A look under the hood. Tornado based search engine." / #PiterPy

Image from [RUS] Artem Malyshev:

[RUS] Artem Malyshev: "Readability matters" / #PiterPy

Image from [RUS] Denis Kataev:

[RUS] Denis Kataev: "Python and binary data" / #PiterPy

Image from [RUS] Dmitry Dygalo:

[RUS] Dmitry Dygalo: "Testable code: making the (testing) world better" / #PiterPy

Image from [RUS] Dmitry Karpov:

[RUS] Dmitry Karpov: "Real-time apps with Python. Tips and tricks" / #PiterPy

Image from [RUS] Dmitry Nazarov:

[RUS] Dmitry Nazarov: "EdgeDB workshop" / #PiterPy

Image from [RUS] Jonas Obrist:

[RUS] Jonas Obrist: "Artisanal Async Adventures" / #PiterPy

Image from [RUS] Juan Luis Cano:

[RUS] Juan Luis Cano: "Catch that asteroid with poliastro!" / #PiterPy

Image from [RUS] Konstantin Ignatov:

[RUS] Konstantin Ignatov: "Python application monitoring: let's do it logging style" / #PiterPy

Image from [RUS] Lena Volzhina:

[RUS] Lena Volzhina: "How to combine physical models, ML and production performance" / #PiterPy

Image from [RUS] Lynn Root:

[RUS] Lynn Root: "asyncio in Practice: We Did It Wrong" / #PiterPy

Image from [RUS] Michal Karzynski:

[RUS] Michal Karzynski: "Developing elegant workflows with Apache Airflow" / #PiterPy

Image from [RUS] Miroslav Šedivý:

[RUS] Miroslav Šedivý: "A Day Has Only 24±1 Hours: import pytz" / #PiterPy

Image from [RUS] Panel discussion: The Future of Python / #PiterPy

[RUS] Panel discussion: The Future of Python / #PiterPy

Image from [RUS] Pascal van Kooten:

[RUS] Pascal van Kooten: "High-Frequency Cryptocurrency Trading using Asyncio" / #PiterPy

Image from [RUS] Piotr Migdal:

[RUS] Piotr Migdal: "Learning neural networks within Jupyter Notebook" / #PiterPy

Image from 50 оттенков celery / Олег Чуркин (TechOps)

50 оттенков celery / Олег Чуркин (TechOps)

Image from A Hacker's Guide to Securing Python Web Applications / Eyitemi Egbejule (NaijaSecForce)

A Hacker's Guide to Securing Python Web Applications / Eyitemi Egbejule (NaijaSecForce)

Image from Beyond jupyter / Андрей Попов (Positive Technologies)

Beyond jupyter / Андрей Попов (Positive Technologies)

Image from Dark Python: Social engineering and hacking with 70% success rate / Lukas Hurych (Twisto.cz)

Dark Python: Social engineering and hacking with 70% success rate / Lukas Hurych (Twisto.cz)

Image from Django under microscope / Артем Малышев (Self Employed)

Django under microscope / Артем Малышев (Self Employed)

Image from Эффективное использование Jupyter и PySpark на Hadoop / Павел Тарасов (ЦИАН)

Эффективное использование Jupyter и PySpark на Hadoop / Павел Тарасов (ЦИАН)

Image from High Performance Data Processing in Python / Donald Whyte (Engineers Gate)

High Performance Data Processing in Python / Donald Whyte (Engineers Gate)

Image from Интерактивный и холиварный доклад про линтеры / Никита Соболев (wemake.services)

Интерактивный и холиварный доклад про линтеры / Никита Соболев (wemake.services)

Image from Как обновить Python 2.7 до Python 3.6 и не умереть / Александр Полищук (Код Безопасности)

Как обновить Python 2.7 до Python 3.6 и не умереть / Александр Полищук (Код Безопасности)

Image from Как учить людей программированию и радоваться жизни / Илья Лебедев (devman.org)

Как учить людей программированию и радоваться жизни / Илья Лебедев (devman.org)

Image from Как выкинуть из проекта C++ код, чтобы за это ничего не было / Александр Боргардт

Как выкинуть из проекта C++ код, чтобы за это ничего не было / Александр Боргардт

Image from Как защитить алгоритм машинного обучения от Adversarial-примеров / Сергей Дудоров (Digital Security)

Как защитить алгоритм машинного обучения от Adversarial-примеров / Сергей Дудоров (Digital Security)

Image from Хорошие и плохие практики для написания тестируемого кода / Дмитрий Дыгало (kiwi.com)

Хорошие и плохие практики для написания тестируемого кода / Дмитрий Дыгало (kiwi.com)

Image from Make Python fast again / Ефрем Матосян (Positive Technologies)

Make Python fast again / Ефрем Матосян (Positive Technologies)

Image from Мастер-класс

Мастер-класс "Эффективная Selenium-инфраструктура" / Иван Крутов (Aerokube)

Image from Опыт использования Apache Kafka в World of Tanks / Левон Авакян (Wargaming)

Опыт использования Apache Kafka в World of Tanks / Левон Авакян (Wargaming)

Image from Открытие Moscow Python Conf++ 2018

Открытие Moscow Python Conf++ 2018

Image from Pylint изнутри. Как он это делает / Максим Мазаев (ЦИАН)

Pylint изнутри. Как он это делает / Максим Мазаев (ЦИАН)

Image from Python Virtual Assistant, или Как собрать бота с AI на простом стеке / Олег Пличко

Python Virtual Assistant, или Как собрать бота с AI на простом стеке / Олег Пличко

Image from Распространенные ошибки в архитектуре Django-приложений / Алексей Половинкин (Agima)

Распространенные ошибки в архитектуре Django-приложений / Алексей Половинкин (Agima)

Image from Refactoring in Python: design patterns and approaches / Tin Marković (Kiwi.com)

Refactoring in Python: design patterns and approaches / Tin Marković (Kiwi.com)

Image from Система ML-моделирования финансовых показателей / Олег Мангутов (Deloitte Analytics Insitute)

Система ML-моделирования финансовых показателей / Олег Мангутов (Deloitte Analytics Insitute)

Image from Создание DSL-компиляторов на Python / Пётр Советов (МИРЭА)

Создание DSL-компиляторов на Python / Пётр Советов (МИРЭА)

Image from Theory of Neural Networks / Beau Carnes (freeCodeCamp.org)

Theory of Neural Networks / Beau Carnes (freeCodeCamp.org)

Image from Ускорение SQLAlchemy для архитектурных космонавтов / Алексей Старков (Qrator Labs)

Ускорение SQLAlchemy для архитектурных космонавтов / Алексей Старков (Qrator Labs)

Image from Видеоотчет о Moscow Python Conf++ 2018

Видеоотчет о Moscow Python Conf++ 2018

Image from Wagtail: когда хочется чего-то приятнее, чем просто Django / Игорь Мосягин (Lamoda)

Wagtail: когда хочется чего-то приятнее, чем просто Django / Игорь Мосягин (Lamoda)

Image from Запись трансляции Moscow Python Conf++ 2018. 22 окт, зал

Запись трансляции Moscow Python Conf++ 2018. 22 окт, зал "Большой"

Image from Запись трансляции Moscow Python Conf++ 2018. 22 окт, зал

Запись трансляции Moscow Python Conf++ 2018. 22 окт, зал "Малый"

Image from Запись трансляции Moscow Python Conf++ 2018. 23 окт, зал

Запись трансляции Moscow Python Conf++ 2018. 23 окт, зал "Большой"

Image from Запись трансляции Moscow Python Conf++ 2018. 23 окт, зал

Запись трансляции Moscow Python Conf++ 2018. 23 окт, зал "Малый"

Image from Запускаем Python-приложение в облака / Евгений Слезко (Marilyn)

Запускаем Python-приложение в облака / Евгений Слезко (Marilyn)

Image from PyData.tokyo One-day Conference 2018 Session1

PyData.tokyo One-day Conference 2018 Session1

Image from PyData.tokyo One-day Conference 2018 Session2

PyData.tokyo One-day Conference 2018 Session2

Image from PyData.tokyo One-day Conference 2018 Session3

PyData.tokyo One-day Conference 2018 Session3

Image from PyData.tokyo One-day Conference 2018 Session4

PyData.tokyo One-day Conference 2018 Session4

Image from PyData.tokyo One-day Conference 2018 Session5

PyData.tokyo One-day Conference 2018 Session5

Image from PyData.tokyo One-day Conference 2018 Session6

PyData.tokyo One-day Conference 2018 Session6

Image from PyData.tokyo One-day Conference 2018 Sponsor Session

PyData.tokyo One-day Conference 2018 Sponsor Session

Image from Keynote - Painel sobre dados abertos e tecnologias cívicas

Keynote - Painel sobre dados abertos e tecnologias cívicas

Image from 2nd Keynote by Sidu Ponnapa

2nd Keynote by Sidu Ponnapa

Image from 3rd Keynote By Travis Oliphant

3rd Keynote By Travis Oliphant

Image from 4th Keynote by Carol Willing

4th Keynote by Carol Willing

Image from Adventures in iterations By Vikrant Patil

Adventures in iterations By Vikrant Patil

Image from Alexa enabled smart home programming with Python By Sonal Raj

Alexa enabled smart home programming with Python By Sonal Raj

Image from Amazing world of animation - powered by python By Sreenivas Alapati

Amazing world of animation - powered by python By Sreenivas Alapati

Image from AQR Quant De-Mystified By Navraj Bhardwaj

AQR Quant De-Mystified By Navraj Bhardwaj

Image from Automating testbed experiments, data collection and visualization with Python By Sunit Kumar Nandi

Automating testbed experiments, data collection and visualization with Python By Sunit Kumar Nandi

Image from Bayesian A/B testing using Python By Vaibhav Pawar

Bayesian A/B testing using Python By Vaibhav Pawar

Image from Building better Python microservices using GRPC By Narendran R

Building better Python microservices using GRPC By Narendran R

Image from Cleaning data with Python By Anand S

Cleaning data with Python By Anand S

Image from Creating 3rd generation Web APIs using Hydra and Hydrus By Akshay Dahiya

Creating 3rd generation Web APIs using Hydra and Hydrus By Akshay Dahiya

Image from Deploying a Python web app onto a Kubernetes Cluster By Tarun Pothulapati

Deploying a Python web app onto a Kubernetes Cluster By Tarun Pothulapati

Image from Django on day 500 By Amit Singh Sethi

Django on day 500 By Amit Singh Sethi

Image from Executing scripts in a few milliseconds with MicroPython By Marwan Al-Sabbagh

Executing scripts in a few milliseconds with MicroPython By Marwan Al-Sabbagh

Image from Experience with Python type hints By Kracekumar Ramaraju

Experience with Python type hints By Kracekumar Ramaraju

Image from FOSSASIA Community Talk By Mario Behling

FOSSASIA Community Talk By Mario Behling

Image from Handling DASH streams - Generation, Consumption & Clipping By Arnav

Handling DASH streams - Generation, Consumption & Clipping By Arnav

Image from How Helpshift built machine learning platform using Python at large scale By Shyam Shinde

How Helpshift built machine learning platform using Python at large scale By Shyam Shinde

Image from Inside the World's Largest Python Course on Coursera By Charles Severance

Inside the World's Largest Python Course on Coursera By Charles Severance

Image from Language Model (Text Analysis) using Python from scratch By Divya Choudhary

Language Model (Text Analysis) using Python from scratch By Divya Choudhary

Image from Large scale web crawling using Python By Anand B Pillai and Noufal Ibrahim

Large scale web crawling using Python By Anand B Pillai and Noufal Ibrahim

Image from Learn Guitar Via Python Programming (MIDI Parsing) By Rishabh Shah and Srinivas Kalyani

Learn Guitar Via Python Programming (MIDI Parsing) By Rishabh Shah and Srinivas Kalyani

Image from [Lightning Talk] At the eye of the flood by Biswaz

[Lightning Talk] At the eye of the flood by Biswaz

Image from Lightning Talk by DBS

Lightning Talk by DBS

Image from Lightning Talk by Enfa Rose George

Lightning Talk by Enfa Rose George

Image from Lightning Talk by Marc Garcia

Lightning Talk by Marc Garcia

Image from Lightning Talk by Yuktesh Chintamadaka (IBM)

Lightning Talk by Yuktesh Chintamadaka (IBM)

Image from [Lightning Talk] Climbing the Minesweeper Leaderboards by Samarth Hattangady

[Lightning Talk] Climbing the Minesweeper Leaderboards by Samarth Hattangady

Image from [Lightning Talk] Cool features of Python 3.7 by Jaysinh Shukla

[Lightning Talk] Cool features of Python 3.7 by Jaysinh Shukla

Image from [Lightning Talk] EEG Controlled Rover - A Brain-Computer Interface by Prajwal and Venkatesh

[Lightning Talk] EEG Controlled Rover - A Brain-Computer Interface by Prajwal and Venkatesh

Image from [Lightning Talk] Failure of the Tests

[Lightning Talk] Failure of the Tests

Image from [Lightning Talk] i-Tagger by Pramati

[Lightning Talk] i-Tagger by Pramati

Image from [Lightning Talk] Machine Learning and AI for the Greater Good by Praveen Pankajakshan (Corteva)

[Lightning Talk] Machine Learning and AI for the Greater Good by Praveen Pankajakshan (Corteva)

Image from [Lightning Talk] Micropython - Python for embedded Systems by Kalpit Champanery

[Lightning Talk] Micropython - Python for embedded Systems by Kalpit Champanery

Image from [Lightning Talk] Python: Batteries Included. Now Bulletproof too by Soroco

[Lightning Talk] Python: Batteries Included. Now Bulletproof too by Soroco

Image from [Lightning Talk] Smart Drowsiness Detection System using Deep Learning by Sai Amrit Patnaik

[Lightning Talk] Smart Drowsiness Detection System using Deep Learning by Sai Amrit Patnaik

Image from [Lightning Talk] Using ChainMaps to simplify CLI argument parsing by Govind

[Lightning Talk] Using ChainMaps to simplify CLI argument parsing by Govind

Image from [Lightning Talk] Using Regular Expressions for parsing Indian Addresses by Yash Chilgupakar

[Lightning Talk] Using Regular Expressions for parsing Indian Addresses by Yash Chilgupakar

Image from [Lightning Talks] Leveraging Communities by Lakshya Sivaramakrishnan

[Lightning Talks] Leveraging Communities by Lakshya Sivaramakrishnan

Image from Managing flood risk in this modern age By Arijit Saha and Atul Singh

Managing flood risk in this modern age By Arijit Saha and Atul Singh

Image from Metaclasses and decorators: a match made in space By Ishan Srivastava

Metaclasses and decorators: a match made in space By Ishan Srivastava

Image from Optimizations in Web Development by Megha Sharma

Optimizations in Web Development by Megha Sharma

Image from Processing videos effectively By R S Nikhil Krishna and Lokesh Kumar T

Processing videos effectively By R S Nikhil Krishna and Lokesh Kumar T

Image from PyCon India 2018 Opening Video by Ramesht Shukla

PyCon India 2018 Opening Video by Ramesht Shukla

Image from Pyflyby: Automatic imports for Python By Karl Chen

Pyflyby: Automatic imports for Python By Karl Chen

Image from Python Project Workflows - Continuous Deployment Friendly By Abhijit Gadgil

Python Project Workflows - Continuous Deployment Friendly By Abhijit Gadgil

Image from Satellite Image Processing with Python By Shubham Sharma

Satellite Image Processing with Python By Shubham Sharma

Image from Speed up your Python modules using Rust By Abhiram Ravikumar

Speed up your Python modules using Rust By Abhiram Ravikumar

Image from Testing micro-services made easy By Devi A S L

Testing micro-services made easy By Devi A S L

Image from The Growth of the Python Community in Africa By Marlene Mhangami

The Growth of the Python Community in Africa By Marlene Mhangami

Image from Understanding Caching in Python By Chirag Shah

Understanding Caching in Python By Chirag Shah

Image from Using Jupyter to Empower the Less Technical By Marc Udoff and Sriram Nagarajan

Using Jupyter to Empower the Less Technical By Marc Udoff and Sriram Nagarajan

Image from Using NLP to demystify 'Terms and Conditions' and summarize the contents By Aroma Rodrigues

Using NLP to demystify 'Terms and Conditions' and summarize the contents By Aroma Rodrigues

Image from VIDEO RESUMEN PYCONES 2018

VIDEO RESUMEN PYCONES 2018

Image from Vote of Thanks - PyCon Team

Vote of Thanks - PyCon Team

Image from What you need to know about data classes in Python 3.7 By Sasidhar Donaparthi

What you need to know about data classes in Python 3.7 By Sasidhar Donaparthi

Image from Agata Skamruk & team - Thanks to sponsors and partners

Agata Skamruk & team - Thanks to sponsors and partners

Image from Łucja Świetlińska i Dagmara Korusiewicz - Dlaczego Scrum nie działa - PyCode Conference 2018

Łucja Świetlińska i Dagmara Korusiewicz - Dlaczego Scrum nie działa - PyCode Conference 2018

Image from Merixstudio - rozstrzygnięcie konkursu - PyCode Conference 2018

Merixstudio - rozstrzygnięcie konkursu - PyCode Conference 2018

Image from Pascal van Kooten - High-Frequency Cryptocurrency Trading using Asyncio - PyCode Conference 2018

Pascal van Kooten - High-Frequency Cryptocurrency Trading using Asyncio - PyCode Conference 2018

Image from 01-204_From Data to Web Application: Anime Character Image Recognition with...(Iskandar Setiadi)

01-204_From Data to Web Application: Anime Character Image Recognition with...(Iskandar Setiadi)

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01-205_Django を Zappaで構築してServerless Python のベストプラクティスを探る(向山裕介/Yusuke Mukoyama)

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01-206_Day2_Lightning Talk~Closing

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03-204_Pythonによる異常検知入門(大山匠)

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03-205_料理写真が美味しく撮れる！開発現場から覗くAI料理カメラの裏側(森永雄也)

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04-205_Pythonで始めるウェブスクレイピング実践入門(田中慎太郎)

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05-203_Python研修の作り方-Teaching Is Learning-(Yusuke Nishio)

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06-204_Python, AWS and FinTech(Pavlos Christoforou)

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02-103_Fun with Python and Kanji(Michael Penkov)

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02-104_Introduce syntax and history of Python from 2.4 to 3.7(Manabu TERADA)

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02-202_Sphinx-2.0 とドキュメントの未来(Takeshi KOMIYA)

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03-101_Applying serverless architecture pattern to distributed data processing(Denys Makogon)

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03-103_The Modern OAuth 2.0(Hsiaoming Yang)

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03-104_Rust と Python(Hideo Hattori)

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03-201_SymPyによる数式処理(Hayao Suzuki)

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04-101_Day1_Opening~Keynote_Argentina in Python: community, dreams,...(Kaufmann Manuel)

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04-104_Webアプリケーションの仕組み(Takayuki Shimizukawa)

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04-105_あなたと私いますぐパッケージン(Atsushi Odagiri)

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04-107_Pythonで解く大学入試数学(新井正貴)

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04-201_Day2_Keynote_「Pythonでやってみた」：広がるプログラミングの愉しみ(磯蘭水)

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04-202_niconicoにおけるコンテンツレコメンドの取り組み(大元司)

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04-203_Djangoアプリケーションにおけるトイル撲滅戦記(Yasuaki Matsuda)

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04-204_Artisanal Async Adventures(Jonas Obrist)

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05-202_Pythonでざっくり学ぶUnixプロセス(tell-k)

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06-101_DjangoではじめるPyCharm実践入門(Kashun Yoshida)

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06-102_Pythonを使ったハードウェア開発について(北神雄太)

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06-103_Interactive Network Visualization using Python 〜 NetworkX + BokehでPEPの参照関係を...(Tomoko Furuki)

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06-202_REST API に疲れたあなたへ贈る GraphQL 入門(Tsukagoshi Keisuke)

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07-101_PyCon JP における子ども向けワークショップの活動事例と実施の意義(Daisuke Saito)

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07-102_Interpretable Machine Learning, making black box models explainable with Python!(David Low)

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07-103_メルカリにおける AI 活用事例(千葉竜介)

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07-104_自分が欲しいものをPythonで書く方法(Python for Myself)(杉山　剛)

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07-201_HomeSecurity with Python(Yuki Takino)

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07-203_Notebook as Web API: Turn your notebook into Web API(横石和貴)

Image from EuroSciPy 2018 SESSION Track 2 - Afternoon (Part 2) 31 08 2018

EuroSciPy 2018 SESSION Track 2 - Afternoon (Part 2) 31 08 2018

Image from EuroSciPy 2018 SESSION TRACK 2 MORNING 31 08 2018

EuroSciPy 2018 SESSION TRACK 2 MORNING 31 08 2018

Image from Friday 31 August - Track 1 - Afternoon (Part 1)

Friday 31 August - Track 1 - Afternoon (Part 1)

Image from Friday 31 August - Track 1 - Afternoon (Part 2)

Friday 31 August - Track 1 - Afternoon (Part 2)

Image from Friday 31 August - Track 1 - Morning

Friday 31 August - Track 1 - Morning

Image from EuroSciPy 2018 SESSION TRACK 2 AFTERNOON PART 2 30 08 2018

EuroSciPy 2018 SESSION TRACK 2 AFTERNOON PART 2 30 08 2018

Image from EuroSciPy 2018 SESSION TRACK 2 AFTERNOON PART 30 08 2018

EuroSciPy 2018 SESSION TRACK 2 AFTERNOON PART 30 08 2018

Image from EuroSciPy 2018 SESSION TRACK 2 MORNING 30 08 2018

EuroSciPy 2018 SESSION TRACK 2 MORNING 30 08 2018

Image from Thursday 30 August - KEYNOTE - How to drive business decisions with AI

Thursday 30 August - KEYNOTE - How to drive business decisions with AI

Image from Thursday 30 August - Track 1 - Afternoon (Part 1)

Thursday 30 August - Track 1 - Afternoon (Part 1)

Image from Thursday 30 August - Track 1 - Afternoon (Part 2)

Thursday 30 August - Track 1 - Afternoon (Part 2)

Image from Thursday 30 August - Track 1 - Morning

Thursday 30 August - Track 1 - Morning

Image from Deep Diving into GANs: From Theory to Production Michele De Simoni, Paolo Galeone 29 08 2018

Deep Diving into GANs: From Theory to Production Michele De Simoni, Paolo Galeone 29 08 2018

Image from Deep Learning in Python using Chainer, C. Loomis 29 08 2018

Deep Learning in Python using Chainer, C. Loomis 29 08 2018

Image from EuroSciPy Advanced Session - CFFI, Ctypes, Cython: the Good, the Bad and the Ugly

EuroSciPy Advanced Session - CFFI, Ctypes, Cython: the Good, the Bad and the Ugly

Image from EuroSciPy Advanced Session - Data visualization: from default to efficient

EuroSciPy Advanced Session - Data visualization: from default to efficient

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EuroSciPy Advanced Session - Parallel Data Analysis with Dask

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EuroSciPy Advanced Session - The Hitchhiker's Guide to Parallelism with Python

Image from EuroSciPy Applications Session Advanced machine learning, Y. Peleg 29 08 2018

EuroSciPy Applications Session Advanced machine learning, Y. Peleg 29 08 2018

Image from EuroSciPy Applications Session Advanced Understanding and diagnosing your machine-learning29 08 2018

EuroSciPy Applications Session Advanced Understanding and diagnosing your machine-learning29 08 2018

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EuroSciPy 2017: Advanced Pandas

Image from EuroSciPy 2017: AMfe - Finite Elements for Structural Dynamics with Simplicity in Mind

EuroSciPy 2017: AMfe - Finite Elements for Structural Dynamics with Simplicity in Mind

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EuroSciPy 2017: Arcas - Using Python to access open research literature

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EuroSciPy 2017: Autoscaling distributed compute with Dask, Kubernetes and AWS

Image from EuroSciPy 2017: AutoWIG: Wrapping very large C++ libraries in Python automatically

EuroSciPy 2017: AutoWIG: Wrapping very large C++ libraries in Python automatically

Image from EuroSciPy 2017: Bayesian Optimization - Can you do better than randomly guessing parameters?

EuroSciPy 2017: Bayesian Optimization - Can you do better than randomly guessing parameters?

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EuroSciPy 2017: Cython

Image from EuroSciPy 2017: Deep Learning for Fraud Detection

EuroSciPy 2017: Deep Learning for Fraud Detection

Image from EuroSciPy 2017: fastmat - A simple Package for Fast Linear Transforms

EuroSciPy 2017: fastmat - A simple Package for Fast Linear Transforms

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EuroSciPy 2017: FluRS - A Library for Streaming Recommendation Algorithms

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EuroSciPy 2017: Fully Convolutional Networks for Image Segmentation

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EuroSciPy 2017: GeoPandas - geospatial data in Python made easy

Image from EuroSciPy 2017: Getting the hang of WASM

EuroSciPy 2017: Getting the hang of WASM

Image from EuroSciPy 2017: Git

EuroSciPy 2017: Git

Image from EuroSciPy 2017: Handshakes, Citizen Science and Evolution

EuroSciPy 2017: Handshakes, Citizen Science and Evolution

Image from EuroSciPy 2017: Interactive 3D Visualization in Jupyter Notebooks

EuroSciPy 2017: Interactive 3D Visualization in Jupyter Notebooks

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EuroSciPy 2017: Introduction into the ppci project

Image from EuroSciPy 2017: Keras

EuroSciPy 2017: Keras

Image from EuroSciPy 2017: Keynote: How to Fix a Scientific Culture: Psychology as a Cautionary Tale

EuroSciPy 2017: Keynote: How to Fix a Scientific Culture: Psychology as a Cautionary Tale

Image from EuroSciPy 2017: Keynote: PyTorch: Framework for fast, dynamic deep learning and scientific computing

EuroSciPy 2017: Keynote: PyTorch: Framework for fast, dynamic deep learning and scientific computing

Image from EuroSciPy 2017: Leverage knowledge from under-represented classes in machine learning

EuroSciPy 2017: Leverage knowledge from under-represented classes in machine learning

Image from EuroSciPy 2017: Lightning Talks

EuroSciPy 2017: Lightning Talks

Image from EuroSciPy 2017: Matplotlib

EuroSciPy 2017: Matplotlib

Image from EuroSciPy 2017: nbval - Testing your notebooks

EuroSciPy 2017: nbval - Testing your notebooks

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EuroSciPy 2017: NetworkX

Image from EuroSciPy 2017: NumPy (1/2)

EuroSciPy 2017: NumPy (1/2)

Image from EuroSciPy 2017: NumPy (2/2)

EuroSciPy 2017: NumPy (2/2)

Image from EuroSciPy 2017: Pandas

EuroSciPy 2017: Pandas

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EuroSciPy 2017: Poster Introductions

Image from EuroSciPy 2017: Pyrate - Optical Raytracing Based on Python

EuroSciPy 2017: Pyrate - Optical Raytracing Based on Python

Image from EuroSciPy 2017: Python (1/2)

EuroSciPy 2017: Python (1/2)

Image from EuroSciPy 2017: Python (2/2)

EuroSciPy 2017: Python (2/2)

Image from EuroSciPy 2017: Real-time Crowd-sourced Detection of Earthquakes

EuroSciPy 2017: Real-time Crowd-sourced Detection of Earthquakes

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EuroSciPy 2017: Scikit-image

Image from EuroSciPy 2017: Scikit-learn (1/2)

EuroSciPy 2017: Scikit-learn (1/2)

Image from EuroSciPy 2017: Scikit-learn (2/2)

EuroSciPy 2017: Scikit-learn (2/2)

Image from EuroSciPy 2017: SciPy

EuroSciPy 2017: SciPy

Image from EuroSciPy 2017: Simphony-Remote - Accessing containerized desktop and web apps with a browser

EuroSciPy 2017: Simphony-Remote - Accessing containerized desktop and web apps with a browser

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EuroSciPy 2017: SymPy

Image from EuroSciPy 2017: Testing the unknown: how to test scientific codes

EuroSciPy 2017: Testing the unknown: how to test scientific codes

Image from EuroSciPy 2017: The OpenTURNS uncertainty quantification Python module

EuroSciPy 2017: The OpenTURNS uncertainty quantification Python module

Image from EuroSciPy 2017: Tricks for Efficient Multicore Computing

EuroSciPy 2017: Tricks for Efficient Multicore Computing

Image from EuroSciPy 2017: Working with Audio Data in Python

EuroSciPy 2017: Working with Audio Data in Python

Image from EuroSciPy Advanced Session

EuroSciPy Advanced Session

Image from EuroSciPy Advanced Session - A tour of Python profiling and optimization

EuroSciPy Advanced Session - A tour of Python profiling and optimization

Image from EuroSciPy Advanced Session - Privacy for Data Scientists

EuroSciPy Advanced Session - Privacy for Data Scientists

Image from EuroSciPy Applications Session

EuroSciPy Applications Session

Image from EuroSciPy Applications Session Handling Geospatial Data using Python, Part II

EuroSciPy Applications Session Handling Geospatial Data using Python, Part II

Image from Friday 31 August - KEYNOTE - Python in medicine: processing, learning and visualization of data

Friday 31 August - KEYNOTE - Python in medicine: processing, learning and visualization of data

Image from Live stream di EuroSciPy Session 1

Live stream di EuroSciPy Session 1

Image from Live stream di EuroSciPy Session 1b

Live stream di EuroSciPy Session 1b

Image from Live stream EusoSciPy2018 session 2

Live stream EusoSciPy2018 session 2

Image from Live stream EusoSciPy2018 session 2b

Live stream EusoSciPy2018 session 2b

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Conference Close

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Democratizing Data Tracy Teal (The Carpentries)

Image from Disease Prediction Using the World's Largest Clinical Lab Dataset Cristian Capdevila (Prognos)

Disease Prediction Using the World's Largest Clinical Lab Dataset Cristian Capdevila (Prognos)

Image from The Reporter's Notebook - Mark Hansen (Columbia Journalism School)

The Reporter's Notebook - Mark Hansen (Columbia Journalism School)

Image from Why Contribute to Open Source? - Julia Meinweld (Two Sigma Investments)

Why Contribute to Open Source? - Julia Meinweld (Two Sigma Investments)

Image from Beyond Interactive: Scaling Impact with Notebooks at Netflix - Michelle Ufford (Netflix)

Beyond Interactive: Scaling Impact with Notebooks at Netflix - Michelle Ufford (Netflix)

Image from Binder: Lowering the bar to sharing interactive software- Tim Head (Wild Tree Tech)

Binder: Lowering the bar to sharing interactive software- Tim Head (Wild Tree Tech)

Image from Business Summit roundtable: The current environment

Business Summit roundtable: The current environment

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Canadians land on Jupyter - Ian Allison, James Colliander

Image from Charles Smith (Netflix) interviewed at JupyterCon NY 2018

Charles Smith (Netflix) interviewed at JupyterCon NY 2018

Image from Containerizing notebooks for serverless execution (sponsored by AWS)

Containerizing notebooks for serverless execution (sponsored by AWS)

Image from Current RISE capabilities and its evolution into the future- Damián Avila (Anaconda, Inc.)

Current RISE capabilities and its evolution into the future- Damián Avila (Anaconda, Inc.)

Image from Dan Mbanga (AWS) interviewed at JupyterCon NY 2018

Dan Mbanga (AWS) interviewed at JupyterCon NY 2018

Image from Data Science as a Catalyst for Scientific Discovery Michelle Gill, Ph.D. (BenevolentAI)

Data Science as a Catalyst for Scientific Discovery Michelle Gill, Ph.D. (BenevolentAI)

Image from Data science in US and Canadian higher education- Laura Noren (Obsidian Security)

Data science in US and Canadian higher education- Laura Noren (Obsidian Security)

Image from Designing for interaction- Scott Sanderson (Quantopian)

Designing for interaction- Scott Sanderson (Quantopian)

Image from Enterprise usage of Jupyter: The business case and best practices for leveraging open source

Enterprise usage of Jupyter: The business case and best practices for leveraging open source

Image from Explorations in reproducible analysis with Nodebook- Kevin Zielnicki (Stitch Fix)

Explorations in reproducible analysis with Nodebook- Kevin Zielnicki (Stitch Fix)

Image from Flipped learning with Jupyter: Experiences, best practices, and supporting research

Flipped learning with Jupyter: Experiences, best practices, and supporting research

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GenePattern Notebook: Jupyter beyond the programmer

Image from Going native: C++ as a first-class citizen of the Jupyter ecosystem

Going native: C++ as a first-class citizen of the Jupyter ecosystem

Image from How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future

How JupyterLab and widgets enable interactive analysis of the Earth's past, present, and future

Image from I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)

I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)

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"If the data will not come to the astronomer. . ." - Adam Thornton (LSST)

Image from Jupyter for every high schooler- Rob Newton (Trinity School)

Jupyter for every high schooler- Rob Newton (Trinity School)

Image from Jupyter & Gravitational Waves - Will Farr (Stony Brook University)

Jupyter & Gravitational Waves - Will Farr (Stony Brook University)

Image from Jupyter in the Enterprise - Luciano Resende (IBM Watson)

Jupyter in the Enterprise - Luciano Resende (IBM Watson)

Image from Jupyter Notebooks and the Intersection of Data Science - David Schaaf (Capital One)

Jupyter Notebooks and the Intersection of Data Science - David Schaaf (Capital One)

Image from Jupyter Trends in 2018 - Paco Nathan (derwen.ai)

Jupyter Trends in 2018 - Paco Nathan (derwen.ai)

Image from JupyterLab and Plotly: A data visualization power couple- Lindsay Richman (McKinsey & Co.)

JupyterLab and Plotly: A data visualization power couple- Lindsay Richman (McKinsey & Co.)

Image from JupyterLab- Ian Rose (UC Berkeley), Chris Colbert (Project Jupyter)

JupyterLab- Ian Rose (UC Berkeley), Chris Colbert (Project Jupyter)

Image from Jupyter's configuration system

Jupyter's configuration system

Image from Keynote by Dan Romuald Mbanga (Amazon Web Services)

Keynote by Dan Romuald Mbanga (Amazon Web Services)

Image from Learn by doing: Using data-driven stories and visualizations in the classroom

Learn by doing: Using data-driven stories and visualizations in the classroom

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Making beautiful objects with Jupyter- M Pacer (Netflix)

Image from nbinteract: Shareable interactive web pages from notebooks

nbinteract: Shareable interactive web pages from notebooks

Image from Notebooks at Netflix: From analytics to engineering- Michelle Ufford (Netflix)

Notebooks at Netflix: From analytics to engineering- Michelle Ufford (Netflix)

Image from Open source software and the allocation of capital- Matt Greenwood (Two Sigma Investments)

Open source software and the allocation of capital- Matt Greenwood (Two Sigma Investments)

Image from Paul Ivanov (Bloomberg) interviewed at JupyterCon NY 2018

Paul Ivanov (Bloomberg) interviewed at JupyterCon NY 2018

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PayPal Notebooks: Data science and machine learning at scale, powered by Jupyter

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Real-time collaboration with Jupyter notebooks using CoCalc- William Stein (SageMath, Inc)

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Reproducible data dependencies for Jupyter - Jackson Brown, Aneesh Karve

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Reproducible education: What teaching can learn from open science practices

Image from Reproducible quantum chemistry in JupyterLab - Chris Harris (Kitware)

Reproducible quantum chemistry in JupyterLab - Chris Harris (Kitware)

Image from Reproducible science with the Renku platform- Sandra Savchenko-de Jong (Swiss Data Science Center)

Reproducible science with the Renku platform- Sandra Savchenko-de Jong (Swiss Data Science Center)

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Scaling notebooks for deep learning workloads - Luciano Resende (IBM Watson)

Image from Scheduled notebooks: A means for manageable and traceable code execution- Matthew Seal (Netflix)

Scheduled notebooks: A means for manageable and traceable code execution- Matthew Seal (Netflix)

Image from SoS: A polyglot notebook and workflow system...- Bo Peng (The University of Texas)

SoS: A polyglot notebook and workflow system...- Bo Peng (The University of Texas)

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Supporting reproducibility in Jupyter through dataflow notebooks

Image from Sustaining Wonder: Jupyter and the Knowledge Commons - Carol Willing (Cal Poly San Luis Obispo)

Sustaining Wonder: Jupyter and the Knowledge Commons - Carol Willing (Cal Poly San Luis Obispo)

Image from SWAN: CERN's Jupyter-based interactive data analysis service - Diogo Castro (CERN)

SWAN: CERN's Jupyter-based interactive data analysis service - Diogo Castro (CERN)

Image from Terraforming Jupyter: Changing JupyterLab to suit your needs

Terraforming Jupyter: Changing JupyterLab to suit your needs

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The Emacs Ipython Notebook- John Miller (Honeywell UOP)

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The Future of Data-driven Discovery in the Cloud - Ryan Abernathey (Columbia University)

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The journey to Julia 1.0: The "Ju" in Jupyter

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The Jupyter Notebook as a transparent way to document machine learning model development

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The reincarnation of a notebook- Tony Fast (Ronin), Nick Bollweg (Georgia Tech Research Institute)

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Using Jupyter notebooks in highly regulated environments

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Using Jupyter to Empower Enterprise Analysts - Dave Stuart (Department of Defense)

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Using the MapD kernel for the Jupyter Notebook- Randy Zwitch (MapD)

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Visualizing high-dimensional biological data with Clustergrammer-Widget in the Jupyter Notebook

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Visualizing machine learning models in the Jupyter Notebook- Chakri Cherukuri (Bloomberg LP)

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What things are correlated with gender diversity: A dig through the ASF and Jupyter projects

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When Jupyter Becomes Pervasive at a University? Fernando Perez (UC Berkeley)

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Станислав Кириллов, CatBoost «CatBoost и Python»

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Travis Oliphant «Brief review of Array computing in Python, where it is heading, and why it matters»

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Василий Литвинов, Intel «Profiling Python and C for fun and profit, or Pandas, go fast!»

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Злата Обуховская, Nvidia «Structured Concurrency. Что не так с асинхронностью в питоне?»

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Adopting Python Asyncio in Large Scale Project (Instagram) – Jimmy Lai – PyCon Taiwan 2018

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Communication strategies beyond the Basic Multilingual Plane – Katie McLaughlin – PyCon Taiwan 2018

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Concurrency vs Parallelism

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從螢幕閱讀程式—NVDA—探討資訊輔助科技與使用者介面可及性發展 – 曾奕勳 – PyCon Taiwan 2018

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Crossing the Python 3 Rubicon – Claudiu Popa – PyCon Taiwan 2018

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Deep Dive into Smart Contracts and Blockchains – Anand Sai, Aditthya R. – PyCon Taiwan 2018

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Deep Learning for NLP: PyTorch vs Tensorflow – Elvis Saravia – PyCon Taiwan 2018

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Easy Way to Build a Real-Time Async Web with Django Channels – Yi-Chieh Chen – PyCon Taiwan 2018

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Educational Code Reviews – 廖偉涵 Adrian Liaw – PyCon Taiwan 2018

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from ai.backend import python – 曾君宇 – PyCon Taiwan 2018

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該怎麼樣（認真的）部署你的 Python Web 應用程式? – Andy Dai – PyCon Taiwan 2018

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Java-Jersey 到 Python-Flask 服務不中斷重構之旅 – Max Lai – PyCon Taiwan 2018

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Lightning Talks and Closing – PyCon Taiwan 2018

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MongoEngine Inside: The Detail of MongoDB & MongoEngine – 郭學聰 (Hsueh-Tsung Kuo) – PyCon Taiwan 2018

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Opening – PyCon Taiwan 2018

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PyCon Taiwan 2018 紀錄片

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Python 工業 4.0—自造工廠監控系統 – Malo – PyCon Taiwan 2018

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Python in Taiwan Photon Source: Motion Control, Data Logger and Chatbot – 林育正 – PyCon Taiwan 2018

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Python 實作運彩下注策略 – 陳建安、廖子慶 – PyCon Taiwan 2018

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人工智慧民主化在台灣 – 陳昇瑋 – PyCon Taiwan 2018

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Smart Things + Home Assistant: Build Your Own Smart Home – Shuhsi Lin – PyCon Taiwan 2018

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土炮一個 Line 股票機器人 – Victor Gau、沈弘哲、Malo – PyCon Taiwan 2018

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Using Tensorflow on Stellar Classification – Poshih Chiang – PyCon Taiwan 2018

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往 MVC GUI 設計邁進—Tkouter—支援 HTML-based Layout 的 Tkinter 擴充函式庫 – 袁克倫 – PyCon Taiwan 2018

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小鴨城 (Duckietown)，一個基於 Raspberry Pi 和 ROS 的開源無人小車專案介紹 – sosorry – PyCon Taiwan 2018

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心靈遊戲: 利用 Python 探索直覺物理 – Jiawei Chen – PyCon Taiwan 2018

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以 Astroquery 套件擷取線上天文觀測資料 – 蘇羿豪 – PyCon Taiwan 2018

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用 Python 來玩粒子物理實驗吧 – 黃坤賢 – PyCon Taiwan 2018

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這樣的開發環境沒問題嗎？ – Tzu-ping Chung – PyCon Taiwan 2018

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自製高擴充性機器學習系統 – 徐愷、曾譯平 – PyCon Taiwan 2018

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PyCon 9 - Lightning Talks

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PyCon Italia 9 - An Italian dark vintage story

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PyCon Italia 9 Goodbye and Closing

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PyCon Italia 9 Welcome

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Pycon Italia meets Concordia

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[PySE 2018] API Star: novo framework REST do criador do DRF

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[PySE 2018] Arquitetura de projetos Flask

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[PySE 2018] Arquitetura do OpenEDX

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[PySE 2018] Bot Command - Automatizando tarefas utilizando Python + Django + Telegram!

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[PySe 2018] Cadê o paradigma? Teoria a serviço de Python na prática

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[PySE 2018] CherryPy - um framework simples, sólido e eficiente

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[PySE 2018] CI/CD - Automatizando a entrega da sua aplicação

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[PySE 2018] Criando aplicações descentralizadas com Python!

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[PySE 2018] Deep NLP - Redes neurais revolucionando o processamento de linguagem natural

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[PySE 2018] Design e Desenvolvimento (Comunicação e Integração)

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[PySE 2018] Encerramento

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[PySE 2018] Event Sourcing em Python

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[PySE 2018] Eventos extremos, rejeição e a cura do salário baixo

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[PySE 2018] Fazendo Música com Sonic Pi

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[PySE 2018] Flora do Campus: identificação e mapeamento das árvores do Campus da UNESP de Rio Claro

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[PySE 2018] GUIs em Python para o seu acelerador de partículas (Projeto Sirius)

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[PySE 2018] Hackeando o setor público com Python

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[PySE 2018] Introdução a algoritmos genéticos

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[PySE 2018] Jornalismo de Dados: Programando a Democracia no Brasil

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[PySE 2018] Jujubot: Implementando um twitter bot da Vovó Juju

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[PySE 2018] Máquinas de estado em Python

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[PySE 2018] Mas cadê as oportunidades Python?

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[PySE 2018] Modelagem de Séries Temporais com Python

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[PySE 2018] NES Hacks 2 - The Mission

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[PySE 2018] Por que Python é a linguagem que todos querem aprender?

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[PySE 2018] Programando a Felicidade

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[PySE 2018] Pytest + Docker: um jeito fácil de ter testes de integração

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[PySE 2018] Python Birds: Aprenda Orientação a Objetos implementando o jogo Angry Birds

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[PySE 2018] Python Web Frameworks - Quais são e como escolher!

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[PySE 2018] Scrapy para usuários de bs4

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[PySE 2018] Weppy - O framework web para humanos

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[PySE 2018][Keynote] Vida Freela: como decolar com a sua carreira de dev freelancer

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[PySE2018] Automatize seus fluxos de trabalho com Airflow

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[PySE2018] De olho no Congresso com Python e Dados Abertos

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[PySE2018] Django Rest Framework - Criando APIs Rest com 3 linhas de código

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[PySE2018] Entendendo um problema silencioso de saúde pública no Brasil e no mundo, o Suicídio

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[PySE2018] Explorando QuerySets do Django

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[PySE2018] O que você precisa saber sobre o básico de uma rede neural

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[PySE2018] Padrões para testes de tela com Python + Selenium e como evitar testes instáveis

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[PySE2018] Pedagogia Tecnológica

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[PySE2018] Python + FabLab - Programando objetos para fabricação digital

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[PySE2018] Python na Astronomia

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[PySE2018][Keynote] Python Moderno

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James Abel & François Piednoël - Meltdown/Spectre for Python Programmers - Pyninsula #10

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Nathaniel J Smith - Python Concurrency for Mere Mortals - Pyninsula #10

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A 1 minute recap of PyCon Belarus 2018 Conference

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Pycon Ph 2018 Day 1

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Pycon Ph 2018 Day 2

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PyCon PH 2018 Erika Fille Legara Ph.D. - Why Python in Scientific Research

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Pycon PH 2018 Howard Lince III - Processing the Social Web

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PyCon PH 2018 Jolly Ann Bilad - Put your analysis to a web app using Dash

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PyCon PH 2018 Joseph Ross Lee - Integrate Two Systems Using Hive Swarm

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PyCon PH 2018 Joshua Arvin Lat - Designing Reliable, Scalable & Secure Data-Intensive Python Apps

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PyCon PH 2018 Mitchel Cabuloy - Write Clean and Maintainable Code with Service Objects

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PyCon PH 2018 Rodney Quillo - Ansible as Configuration Management: Features and Beyond

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PyCon PH 2018 Sony Valdez Intro to Python

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PyCon PH 2018 Younggun Kim - Python Community and Olympism

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Lightning talks

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PyCaribbean 2018 - Closing Video

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avc Geospatial data science and analysis using ArcGIS API for Python

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Boosting Python Web Applications with Protocol Buffers and GRPC

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Building microservices with firefly

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Building single page javascript apps with Django, Graphql, Relay and React!

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Clean Architecture Applications in Python

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Django on Steroids Building Applications at Web Scale

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Getting Started with Embedded Python MicroPython and CircuitPython

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How import works in Python

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How is metaclass used in real life

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How to Boost your Tensorflow model inference performance using Asyncio

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HTTP Bottom Up Live!

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Keynote - Python And Data: Past, Present And Future By Peter Wang

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Liberating tabular data from the clutches of PDFs

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Machine learning & NLP at Goibibo Keytopic extraction and contextual sentiment analysis of 20 millio

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Making Of NextGen Fintech: Insights From Visible Alpha Tech Stack

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Mentoring keynote Noufal Ibrahim

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OLMONK Data validation package

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Panel discussion : Women in Open source

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PyBeacon Eddystone Protocol implementation in Python

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python community principle keynote Elizabeth Ferrao

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Reducing dead code ratio of your project with vulture

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Spinning local DNS server sourcing responses over HTTPS to combat Man in the middle attack

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tools to define Machine Learning

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Using Python and microservices to fuel WebPush at Mozilla

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Analysing flight safety data with Python - Jesús Martos, Alejandro Saez Mollejo

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BigData and ML@Google - Keynote Jarek Kuśmierek

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Building People Analytics - Keynote Cameron Davidson-Pilon

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Closing Notes PyData Warsaw 2017

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Developing Data Science products - Jan Zyśko, Magdalena Kalbarczyk

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Lightning Talk 1: A Python discovery engine

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Lightning Talk 2: Fighting Friedman's Curve - towards data driven childbirth assessment

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Lightning Talk 3 - James Powell

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Lightning Talk 4: Preserve window layout in PyCharm

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Lightning Talk 6: Code or die

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Maxing out supervised learning - Artur Suchwałko, Tomasz Melcer

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Mining articles for practical insight for content creation - Łukasz Dziekan, Michał Stolarczyk

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Natural language processing challenges - Łukasz Kobyliński, Michał Wasiluk

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Opening Notes PyData Warsaw 2017

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Sport analysis with Python - ThuyLe

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Towards interpretable reliable models - Keynote Katharine Jarmul

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Use of word embedding algorithms and siamese recurrent neural networks - Mikołaj Sędek, Rafał Wojdan

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Why does my girlfriend dislike my music? - Juan De Dios Santos Rivera

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Winning together: Bridging the gap between academia and industry - Keynote Radim Řehůřek

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Lightning Talks

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Lightning Talks Manhã

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Lightning Talks Tarde

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Paola Katherine Dualcam

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PyCon-KE 2017 - Demystifying (or Unblackboxing) Neural Networks by Kathleen Siminyu

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PyCon-KE 2017 - Lanes and Traffic signs detection for self driving cars by Wilfred Githuka Gachugi

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PyCon-KE 2017 - Rust for Python(istas) by Matt Gathu

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Seun Awoyele(DevCenter) - Working Better as a Developer

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Sophia Irozuru (Bridge Academy)- My Python training Experience

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AWS APIGateway + Python Lambda + NEologdで作るサーバレス日本語形態素解析API (Satoru Kadowaki) - PyCon JP 2017

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Day 1 Opening 〜 Keynote (Peter Wang) - PyCon JP 2017

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Day 2 Lightning Talks ～ Closing - PyCon JP 2017

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クイズ・python勝ち抜きバトル!! (Motohiro Ueki) - PyCon JP 2017

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メディア会議 - PyCon JP 2017

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Pythonにおけるデバッグ手法 (TakesxiSximada) - PyCon JP 2017

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Pythonをとりまく並行/非同期の話 (tell- k) - PyCon JP 2017

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SREエンジニアがJupyter＋BigQueryでデータ分析基盤をDev＆Opsする話 (yuzutas0) - PyCon JP 2017

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Using machine learning to try and predict taxi availability (Hari Allamraju) - PyCon JP 2017

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早稲田大学グローバルソフトウェアエンジニアリング研究所トークセッション - PyCon JP 2017

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Clearer Code at Scale: Static Types at Zulip and Dropbox (Greg Price) - PyCon JP 2017

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Day 1 Lightning Talks ～ Closing - PyCon JP 2017

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Day 2 Opening ～ Keynote (Masaaki Horikoshi) - PyCon JP 2017

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ディープニューラルネット確率的プログラミングライブラリ Edward (Yuta Kashino) - PyCon JP 2017

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ドローンのフライトコントローラをPythonで制御してみた話 (KATAYOSE LINA) PyCon JP 2017

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Geospatial data analysis and visualization in Python (Halfdan Rump) - PyCon JP 2017

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How (and Why) We Speak in Unicode (Devon Peticolas) - PyCon JP 2017

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機械学習におけるデータの再現性について (Yuichiro Someya) - PyCon JP 2017

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OpenAPIを利用したPythonWebアプリケーション開発 (Takuro Wada) - PyCon JP 2017

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Polyphony: Python ではじめる FPGA と CNN への応用 (Ryos Suzuki) - PyCon JP 2017

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プロダクト開発して分かったDjangoの深～いパーミッション管理の話 (Hiroki Kiyohara) - PyCon JP 2017

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Python におけるドメイン駆動設計(戦術面)の勘どころ (Junya Hayashi) - PyCon JP 2017

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Python: Super Power for Blended Learning (Rizky Ariestiyansyah) - PyCon JP 2017

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Pythonで実現する4コマ漫画の分析・評論 2017 (SHINJI KITAGAWA) - PyCon JP 2017

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Pythonistaで始めるiOSプロトタイプ開発 (Yusuke Muraoka) - PyCon JP 2017

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Pythonの本気！RaspberryPiやEdisonを使ったIoTシステムの構築 (Yuta Kitagami) - PyCon JP 2017

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Pythonと機械学習によるWebセキュリティの自動化 (Isao Takaesu) - PyCon JP 2017

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Reactive Programming on AWS Lambda in Python (Keith Yang) - PyCon JP 2017

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Running Dask in the Cloud (Shane Cousins) - PyCon JP 2017

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Secrets of a WSGI master. (Graham Dumpleton) - PyCon JP 2017

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Sharding with SQLAlchemy (Akira Matsuzaki) - PyCon JP 2017

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The theory of Serverless development by Python (Masashi TERUI) - PyCon JP 2017

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after VOEZ launch: how to resolve problems of mobile game serve... (Hsueh-Tsung Kuo) - PyCon JP 2017

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ベンリに使おう変数アノテーション - typing.pyとの楽しいお付き合い (Takumi Sueda) - PyCon JP 2017

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Building a Customized Personal Assistant with Python (Iskandar Setiadi) - PyCon JP 2017

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Djangoフレームワークのユーザーモデルと認証 (Shinya Okano) - PyCon JP 2017

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Industrial Test Automation with Asyncio (Justus Perlwitz) - PyCon JP 2017

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Kivyによるアプリケーション開発のすすめ (Jun okazaki) - PyCon JP 2017

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len()関数がオブジェクトの長さを手にいれる仕組み (Takayuki Shimizukawa) - PyCon JP 2017

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Pythonで大量データ処理！PySparkを用いたデータ分析のきほん (chie hayashida) - PyCon JP 2017

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PythonでOAuth『サーバ』を構築した話 (akira taniguchi) - PyCon JP 2017

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Pythonによる文章自動生成入門！Python ✖︎ 自然言語処理 ✖︎ ディープラーニング (Hiromitsu Ota) - PyCon JP 2017

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Pythonとパッケージングと私 (Atsushi Odagiri) - PyCon JP 2017

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PythonとRを行ったり来たり (Toshiyuki Takeda) - PyCon JP 2017

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Why you should do text analysis with Python (Bhargav Srinivasa Desikan) - PyCon JP 2017

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野球を科学する技術〜Pythonを用いた統計ライブラリ作成と分析基盤構築 (Shinichi Nakagawa) - PyCon JP 2017

Image from Pycon 2017 closing and remarks

Pycon 2017 closing and remarks

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Accelerating data-driven culture at the largest media group in Latin America with Jupyter

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Beautiful networks and network analytics made simpler with Jupyter - Daina Bouquin, John DeBlase

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Building a notebook platform for 100,000 users - Scott Sanderson (Quantopian)

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Building Interactive Applications and Dashboards in the Jupyter Notebook

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Citing the Jupyter Notebook in the scientific publication process - Bernie Randles, Hope Chen

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Closing the gap between Jupyter and academic publishing - Mark Hahnel, Marius Tulbure

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Cloud Datalab: Jupyter with the power of BigQuery and TensorFlow-Kazunori Sato (Google)

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Collaboration and Automated Operation as Literate Computing for Reproducible Infrastructure

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Data science apps: Beyond notebooks - Natalino Busa (Teradata)

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Data Science at UC Berkeley: 2,000 Undergraduates, 50 Majors, No Command Line

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Data Science Encapsulation and Deployment with Anaconda Project and JupyterLab

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Data science made easy in Jupyter notebooks using PixieDust and InsightFactory - IBM Watson Health)

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Data Science Platforms: Your Key to Actionable Analytics - William Merchan (DataScience.com)

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Data science without borders - Wes McKinney (Two Sigma Investments)

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Deep Learning and Elastic GPUs Using Jupyter - Tim Gasper, Subbu Rama (Bitfusion)

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Defactoring Pace of Change: Reviewing Computational Research in the Digital Humanities

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Demba Ba interviewed at O'Reilly JupyterCon

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Democratizing Access to Open Data by Providing Open Computational Infrastructure

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Deploying a Reproducible Course

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Design for reproducibility - Lorena Barba (George Washington University)

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Empower Scientists; Save Humanity: NumFOCUS

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Enhancing Data Journalism with Jupyter - Karlijn Willems (DataCamp)

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From Beaker to Beaker - Matt Greenwood (Two Sigma Investments)

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Fueling Open Innovation in a Data-Centric World - Peter Wang (Anaconda)

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GenePattern Notebook: Jupyter for integrative genomics - Thorin Tabor (University of California)

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How Jupyter Makes Experimental and Computational Collaborations Easy

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How JupyterHub Tamed Big Science - Cholia, Thomas, Canon (Lawrence Berkeley National Laboratory)

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Humans in the Loop: Jupyter Notebooks as a Frontend for AI Pipelines at Scale

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Industry and Open Source: Working together to Drive Advancements in Jupyter

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Inspirational Real-World Examples and Reusable Industry Notebooks

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Interactive data exploration with Jason Grout (Bloomberg)

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Jupyter at Netflix - Kyle Kelley (Netflix)

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Jupyter at O'Reilly - Andrew Odewahn (O'Reilly Media)

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Jupyter Frontends: From the Classic Jupyter Notebook to JupyterLab, nteract, and Beyond

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Jupyter is democratizing the art of online creation with Peter Wang (Continuum Analytics)

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Jupyter: Kernels, Protocols, and the IPython Reference Implementation

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Jupyter Notebooks and Production Data Science Workflows - Andrew Therriault (City of Boston)

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JupyterHub: A Roadmap of Recent Developments and Future Directions

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JupyterLab: The Next-Generation Jupyter Frontend

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Learning to Code Isn’t Enough: Training as a Pathway to Improve Diversity

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Lessons Learned from Tens of Thousands of Kaggle Notebooks

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Making science happen faster - Jeremy Freeman (Chan Zuckerberg Initiative)

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Managing a 1,000+ Student JupyterHub without Losing Your Sanity

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Mapping Data in Jupyter Notebooks with PixieDust - Raj Singh (IBM Cloud Data Services)

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Music and Jupyter: A Combo for Creating Collaborative Narratives

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Opinionated analysis development - Hilary Parker (Stitch Fix)

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Postpublication Peer Review of Jupyter Notebooks Referenced in Articles on PubMed Central

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Reproducible Dashboards and other great things to do with Jupyter - Mac Rogers (Domino Data Lab)

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Scala: Why hasn't an Official Scala Kernel for Jupyter emerged yet?

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Teaching from Jupyter Notebooks - Christian Moscardi (The Data Incubator)

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The give and take of open source - Brett Cannon (Microsoft | Python Software Foundation)

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The Jupyter Notebook as Document: From Structure to Application

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Three movements driving enterprise adoption of Jupyter - William Merchan (DataScience.com)

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Using Jupyter at the Intersection of Robots and Industrial Biology - Danielle Chou (Zymergen)

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Using Jupyter’s Interactiveness to Build Better Predictive Models

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Where money meets open source - Nadia Eghbal (GitHub)

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Writing (and publishing) a Book Written in Jupyter Notebooks - Andreas Mueller (Columbia University)

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Xeus: A Framework for Writing Native Jupyter Kernels

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Image from 심상진: Genetic Programming을 이용한 Machine Learning 모델 만들기

심상진: Genetic Programming을 이용한 Machine Learning 모델 만들기

Image from 신정규: 전자양의 꿈을 꾸는 안드로이드: Python과 NLTK, TensorFlow를 이용한 챗봇 감정모형 구현

신정규: 전자양의 꿈을 꾸는 안드로이드: Python과 NLTK, TensorFlow를 이용한 챗봇 감정모형 구현

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신예지: 데이터로 보는 파이콘

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송지형: Infrastructure as Code 를 위한 Ansible 활용 방법

Image from 윤제상: Tiamat: Python으로 만드는 AWS Provisioning Tool

윤제상: Tiamat: Python으로 만드는 AWS Provisioning Tool

Image from 윤상웅 - 구름이 하늘의 일이라면: Python과 TensorFlow를 이용한 기상예측

윤상웅 - 구름이 하늘의 일이라면: Python과 TensorFlow를 이용한 기상예측

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유서원: Practical automation for beginners

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Charla patrocinador

Image from 陳坤裕 - Python 開源軟體考古 - 以 Viper 為例 - PyConTW2017

陳坤裕 - Python 開源軟體考古 - 以 Viper 為例 - PyConTW2017

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陳縕儂 - 對話機器人的腦子與靈魂 Bot's Brain and Soul - PyConTW2017

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Image from enjoy type hinting and its benefits – masahito – PyCon Taiwan 2017

enjoy type hinting and its benefits – masahito – PyCon Taiwan 2017

Image from Flying over Taipei with Van Gogh and His Friends – Peter Yeh – PyCon Taiwan 2017

Flying over Taipei with Van Gogh and His Friends – Peter Yeh – PyCon Taiwan 2017

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郭學聰-AfterVOEZ Launch:How toResolveProblems ofMobileGameServerDevelopment andServiceMaintenance

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Jalem Raj Rohit - Understanding Serverless Architecture

Image from Keynote – Building for Failure: Learning Lessons from Aviation – Andrew Godwin – PyCon Taiwan 2017

Keynote – Building for Failure: Learning Lessons from Aviation – Andrew Godwin – PyCon Taiwan 2017

Image from Keynote – Choices for Smarter AI – 林軒田 – PyCon Taiwan 2017

Keynote – Choices for Smarter AI – 林軒田 – PyCon Taiwan 2017

Image from Keynote – From Little Things, Big Things Grow – Russell Keith-Magee – PyCon Taiwan 2017

Keynote – From Little Things, Big Things Grow – Russell Keith-Magee – PyCon Taiwan 2017

Image from Keynote – The State of Python for Education – Carol Willing – PyCon Taiwan 2017

Keynote – The State of Python for Education – Carol Willing – PyCon Taiwan 2017

Image from 梁智程 - PoS：環保又安全的區塊鏈共識機制 - PyConTW2017

梁智程 - PoS：環保又安全的區塊鏈共識機制 - PyConTW2017

Image from 林鉦育 - Chatbot @ E Sun Bank – 玉山小i 隨身金融顧問的兩三事 - PyConTW2017

林鉦育 - Chatbot @ E Sun Bank – 玉山小i 隨身金融顧問的兩三事 - PyConTW2017

Image from 劉純睿(阿吉) - 比美麗的湯更美麗: pyquery - PyConTW2017

劉純睿(阿吉) - 比美麗的湯更美麗: pyquery - PyConTW2017

Image from 模仿遊戲: AI如何與人類互動獲得新技能 – Jiawei Chen – PyCon Taiwan 2017

模仿遊戲: AI如何與人類互動獲得新技能 – Jiawei Chen – PyCon Taiwan 2017

Image from OpenCLGA - Run Your GA on Top of PyOpenCL – John Hu (胡訓誠) – PyCon Taiwan 2017

OpenCLGA - Run Your GA on Top of PyOpenCL – John Hu (胡訓誠) – PyCon Taiwan 2017

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潘穎達(Panda), 陳柏瑋 - 用Python成為網路投資王 - PyConTW2017

Image from PyPy's approach to construct domain-specific language runtime – TsundereChen – PyCon Taiwan 2017

PyPy's approach to construct domain-specific language runtime – TsundereChen – PyCon Taiwan 2017

Image from 千百種面對 PDF 的方法 – 廖偉涵 Adrian Liaw – PyCon Taiwan 2017

千百種面對 PDF 的方法 – 廖偉涵 Adrian Liaw – PyCon Taiwan 2017

Image from 淺嚐LHCb數據分析的滋味 Play around the LHCb Data on Kaggle – 趙元 (Yuan CHAO) – PyCon Taiwan 2017

淺嚐LHCb數據分析的滋味 Play around the LHCb Data on Kaggle – 趙元 (Yuan CHAO) – PyCon Taiwan 2017

Image from Recognising people in videos using a pre-trained d – Chia-Chun Lu – PyCon Taiwan 2017

Recognising people in videos using a pre-trained d – Chia-Chun Lu – PyCon Taiwan 2017

Image from sosorry - Learn LoRa with Python - PyConTW2017

sosorry - Learn LoRa with Python - PyConTW2017

Image from Stranger in a Strange Land – Russell Keith-Magee – PyCon Taiwan 2017

Stranger in a Strange Land – Russell Keith-Magee – PyCon Taiwan 2017

Image from 蘇羿豪 - 天文黑客們的 Python 大冒險 - PyConTW2017

蘇羿豪 - 天文黑客們的 Python 大冒險 - PyConTW2017

Image from Tensorflow & Python: Fault Detection System – Eric Byungwook Ahn – PyCon Taiwan 2017

Tensorflow & Python: Fault Detection System – Eric Byungwook Ahn – PyCon Taiwan 2017

Image from Tzung-Bi Shih - Global Interpreter Lock Episode III - PyConTW2017

Tzung-Bi Shih - Global Interpreter Lock Episode III - PyConTW2017

Image from Write Elegant Concurrent Code in Python – Mosky Liu – PyCon Taiwan 2017

Write Elegant Concurrent Code in Python – Mosky Liu – PyCon Taiwan 2017

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吳志忠 - 利用 Python 與人工智慧快速打造人性化聊天機器人 - PyConTW2017

Image from 蕭聖穎 - Don't reinvent Sandwich Decorator and with statement - PyConTW2017

蕭聖穎 - Don't reinvent Sandwich Decorator and with statement - PyConTW2017

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徐愷 - 整合 Slack 與 Docker 搭建 Jupyter 線上程式面試系統 - PyConTW2017

Image from 許邱翔 - Python module in Rust - PyConTW2017

許邱翔 - Python module in Rust - PyConTW2017

Image from Yi-Chieh Chen - 用 Django 建立一個可以設定爬蟲排程任務與監控的網站 - PyConTW2017

Yi-Chieh Chen - 用 Django 建立一個可以設定爬蟲排程任務與監控的網站 - PyConTW2017

Image from 遺傳演算法最佳化高頻交易策略 – 林萍珍 – PyCon Taiwan 2017

遺傳演算法最佳化高頻交易策略 – 林萍珍 – PyCon Taiwan 2017

Image from 莊鐵鴻 - Amazing GANs - PyConTW2017

莊鐵鴻 - Amazing GANs - PyConTW2017

Image from [自py系列2] 投資策略驗證系統 – Kilik Kuo – PyCon Taiwan 2017

[自py系列2] 投資策略驗證系統 – Kilik Kuo – PyCon Taiwan 2017

Image from Zong-han, Xie - Practical Data Transformation and Analysis with Pandas - PyConTW2017

Zong-han, Xie - Practical Data Transformation and Analysis with Pandas - PyConTW2017

Image from Keynote: Álvaro

Keynote: Álvaro "Turicas"

Image from Keynote: Annanda Souza

Keynote: Annanda Souza

Image from Keynote: Cláudio Berrondo

Keynote: Cláudio Berrondo

Image from Palestra: André Fonseca - Microserviços e o problema dos dados distribuídos

Palestra: André Fonseca - Microserviços e o problema dos dados distribuídos

Image from Palestra: Andressa Sivolella - Faça kung-fu com Pandas!

Palestra: Andressa Sivolella - Faça kung-fu com Pandas!

Image from Palestra: Douglas Bastos - Descomplicando os mocks

Palestra: Douglas Bastos - Descomplicando os mocks

Image from Palestra: Eduardo Cuducos - Vamos mockar tudo!

Palestra: Eduardo Cuducos - Vamos mockar tudo!

Image from Palestra: Felipe Ximenes - [Quase] Tudo que você precisa saber sobre tarefas assíncronas

Palestra: Felipe Ximenes - [Quase] Tudo que você precisa saber sobre tarefas assíncronas

Image from Palestra: Júlio Cesar Melanda - Aprendi Python, e agora?

Palestra: Júlio Cesar Melanda - Aprendi Python, e agora?

Image from Palestra: Luciano Ratamero - Como usar Django nos tempos de Angular e React

Palestra: Luciano Ratamero - Como usar Django nos tempos de Angular e React

Image from Palestra: Samuel Sampaio (Samuka) - Como organizar sua infraestrutura com soluções pythônicas

Palestra: Samuel Sampaio (Samuka) - Como organizar sua infraestrutura com soluções pythônicas

Image from Palestra: Susana Bouchardet - Big Data: Iniciando meu trabalho com grandes dados

Palestra: Susana Bouchardet - Big Data: Iniciando meu trabalho com grandes dados

Image from PyData London 2017 - Closing Notes

PyData London 2017 - Closing Notes

Image from Alex Orlov — Cython at Instagram

Alex Orlov — Cython at Instagram

Image from Andrew Svetlov — Making OSS library

Andrew Svetlov — Making OSS library

Image from Day 2, Alexander Fedunov's lightning talk

Day 2, Alexander Fedunov's lightning talk

Image from Day 2, Artyom Malyshev's lightning talk

Day 2, Artyom Malyshev's lightning talk

Image from Day 2, Mikhail Krivushin's lightning talk

Day 2, Mikhail Krivushin's lightning talk

Image from Day 2, Sergey Russkih's lightning talk

Day 2, Sergey Russkih's lightning talk

Image from Day 2, Serhiy Masutin's lightning talk

Day 2, Serhiy Masutin's lightning talk

Image from Day 2, Valeriy Zamarayev's lightning talk

Day 2, Valeriy Zamarayev's lightning talk

Image from day 2, Vladimir Kyrylov's lightning talk

day 2, Vladimir Kyrylov's lightning talk

Image from Hynek Schlawack — Solid Snakes or: How to Take 5 Weeks of Vacation

Hynek Schlawack — Solid Snakes or: How to Take 5 Weeks of Vacation

Image from Ievgen Terpil — Find insights in graphs with Python

Ievgen Terpil — Find insights in graphs with Python

Image from Kirill Perevozchikov — Python package with non-python dependencies on load test tool example

Kirill Perevozchikov — Python package with non-python dependencies on load test tool example

Image from lightning talk 1, day 1

lightning talk 1, day 1

Image from lightning talk 2, day 1

lightning talk 2, day 1

Image from lightning talk 3, day 1

lightning talk 3, day 1

Image from lightning talk 4, day 1

lightning talk 4, day 1

Image from lightning talk 5, day 1

lightning talk 5, day 1

Image from Serhii Ruskykh — Композиция объектов - ключ к “дзену”

Serhii Ruskykh — Композиция объектов - ключ к “дзену”

Image from Vitaly Androsenko — Python ABAC implementation

Vitaly Androsenko — Python ABAC implementation

Image from Vsevolod Solovyov — How to choose a database

Vsevolod Solovyov — How to choose a database

Image from PyData Warsaw Live Stream 00

PyData Warsaw Live Stream 00

Image from PyData Warsaw Live Stream 01

PyData Warsaw Live Stream 01

Image from PyCon PH 2017 - An Introduction to Distributed Task Queue by Ronald Ryan A. Icasiano

PyCon PH 2017 - An Introduction to Distributed Task Queue by Ronald Ryan A. Icasiano

Image from PyCon PH 2017 - Big Data Processing and Data Science Environment in Python by Rick Bahague

PyCon PH 2017 - Big Data Processing and Data Science Environment in Python by Rick Bahague

Image from PyCon PH 2017 - Building communities: One Byte at a Time by Manoj Pandey

PyCon PH 2017 - Building communities: One Byte at a Time by Manoj Pandey

Image from PyCon PH 2017 - Estimating Solar Energy of an Area in Python by Justine Santos and Patrick Lucañas

PyCon PH 2017 - Estimating Solar Energy of an Area in Python by Justine Santos and Patrick Lucañas

Image from PyCon PH 2017 - Gentle Introduction to Ansible by Romar Mayer Micabalo

PyCon PH 2017 - Gentle Introduction to Ansible by Romar Mayer Micabalo

Image from PyCon PH 2017 - Going Serverless With Chalice - A Python Serverless Microframework by Roselle Ebarle

PyCon PH 2017 - Going Serverless With Chalice - A Python Serverless Microframework by Roselle Ebarle

Image from PyCon PH 2017 - Intro to Satellite Data Processing Using Python by Kim Ramos and Romer Kristi Aranas

PyCon PH 2017 - Intro to Satellite Data Processing Using Python by Kim Ramos and Romer Kristi Aranas

Image from PyCon PH 2017 - IoT Mobile Robot Using Micropython by Diogenes Pascua

PyCon PH 2017 - IoT Mobile Robot Using Micropython by Diogenes Pascua

Image from PyCon PH 2017 - Messenger BOT using Flask API by Jethro Guce

PyCon PH 2017 - Messenger BOT using Flask API by Jethro Guce

Image from PyCon PH 2017 - Practical Deployment Strategies for Production Python Apps by Joshua Arvin Lat

PyCon PH 2017 - Practical Deployment Strategies for Production Python Apps by Joshua Arvin Lat

Image from PyCon PH 2017 - Pytest + Selenium: A Match for Testing by Marisa Quiaot and Lovely Banquil

PyCon PH 2017 - Pytest + Selenium: A Match for Testing by Marisa Quiaot and Lovely Banquil

Image from PyCon PH 2017 - Python as a Universal Backend by Kix Panganiban

PyCon PH 2017 - Python as a Universal Backend by Kix Panganiban

Image from PyCon PH 2017 - Python in Smart Farm by Lex Bryan

PyCon PH 2017 - Python in Smart Farm by Lex Bryan

Image from PyCon PH 2017 - Python on the Philippine Microsatellite Program by Benjie Jiao

PyCon PH 2017 - Python on the Philippine Microsatellite Program by Benjie Jiao

Image from PyCon PH 2017 - Python Platforms for Cyber Security by Nikolay Akatyev and Brandon Schmittling

PyCon PH 2017 - Python Platforms for Cyber Security by Nikolay Akatyev and Brandon Schmittling

Image from PyCon PH 2017 - Test Driven Development in Python by Elpedio Adoptante Jr. and Zakatell Kanda

PyCon PH 2017 - Test Driven Development in Python by Elpedio Adoptante Jr. and Zakatell Kanda

Image from PyCon PH 2017 - Trip Down Memory Lane: OOM in Python by Prem Vilas Fortran Rara

PyCon PH 2017 - Trip Down Memory Lane: OOM in Python by Prem Vilas Fortran Rara

Image from PyCon PH 2017 - Wela School System Using Python by Sean Mark Mira and John Vincent Fiel

PyCon PH 2017 - Wela School System Using Python by Sean Mark Mira and John Vincent Fiel

Image from PyCon PH 2017 - Writing Idiomatic Python by Jeff Knupp

PyCon PH 2017 - Writing Idiomatic Python by Jeff Knupp

Image from PyCon PH 2017 - Writing RESTful APIs with Flask by Allan Barredo

PyCon PH 2017 - Writing RESTful APIs with Flask by Allan Barredo

Image from Intro video for PyCaribbean 2017

Intro video for PyCaribbean 2017

Image from Lighting talks

Lighting talks

Image from Pycaribbean final video

Pycaribbean final video

Image from Reach More People SMS Data Collection with RapidPro

Reach More People SMS Data Collection with RapidPro

Image from PyCon mini Hiroshima 2016 (1)

PyCon mini Hiroshima 2016 (1)

Image from PyCon mini Hiroshima 2016 (2)

PyCon mini Hiroshima 2016 (2)

Image from PyCon mini Hiroshima 2016 (3)

PyCon mini Hiroshima 2016 (3)

Image from PyCon mini Hiroshima 2016 (4)

PyCon mini Hiroshima 2016 (4)

Image from Depoimento incrível da Cristiane e da Raquel sobre a Python Brasil

Depoimento incrível da Cristiane e da Raquel sobre a Python Brasil

Image from Encerramento - Python Brasil 12

Encerramento - Python Brasil 12

Image from Job Fair - Geekhunter

Job Fair - Geekhunter

Image from Job Fair - MobLee

Job Fair - MobLee

Image from lightning talk - MicroPython

lightning talk - MicroPython

Image from lightning talk - PyBabies

lightning talk - PyBabies

Image from PyBay2016 Highlights

PyBay2016 Highlights

Image from Saturday: Lightning Talks

Saturday: Lightning Talks

Image from A tale of Ansible fanboy - PyConSG 2016

A tale of Ansible fanboy - PyConSG 2016

Image from Building a simple-to-use Database Management tool - PyConSG 2016

Building a simple-to-use Database Management tool - PyConSG 2016

Image from Creating map visualizations with open data and Folium - PyConSG 2016

Creating map visualizations with open data and Folium - PyConSG 2016

Image from Customer Segmentation in Python - PyConSG 2016

Customer Segmentation in Python - PyConSG 2016

Image from Deep Learning Hands-on Workshop - PyConSG 2016

Deep Learning Hands-on Workshop - PyConSG 2016

Image from Deep Learning With Python & Tensorflow - PyConSG 2016

Deep Learning With Python & Tensorflow - PyConSG 2016

Image from Deploying scalable Django application on AWS - PyConSG 2016

Deploying scalable Django application on AWS - PyConSG 2016

Image from Design and implement a scalable and high performance tcp server with tornado - PyConSG 2016

Design and implement a scalable and high performance tcp server with tornado - PyConSG 2016

Image from Keynote by Dr. Yves J. Hilpisch - PyConSG 2016

Keynote by Dr. Yves J. Hilpisch - PyConSG 2016

Image from Keynote: How to Write a Popular Python Library by Accident - PyConSG 2016

Keynote: How to Write a Popular Python Library by Accident - PyConSG 2016

Image from Lightning Talk: Bitcoin mining on Python - PyConSG 2016

Lightning Talk: Bitcoin mining on Python - PyConSG 2016

Image from Lightning Talk: Openshift - PyConSG 2016

Lightning Talk: Openshift - PyConSG 2016

Image from Lightning Talk: Pycon Malaysia - PyConSG 2016

Lightning Talk: Pycon Malaysia - PyConSG 2016

Image from Lightning Talk: Python Decorators - PyConSG 2016

Lightning Talk: Python Decorators - PyConSG 2016

Image from Lightning Talk: Singapore Python User Group - PyConSG 2016

Lightning Talk: Singapore Python User Group - PyConSG 2016

Image from Lightning Talk: Software Carpentry - PyConSG 2016

Lightning Talk: Software Carpentry - PyConSG 2016

Image from Lightning Talk: Sprints - PyConSG 2016

Lightning Talk: Sprints - PyConSG 2016

Image from Machine Learning Pipeline using Luigi and Scikit Learn - PyConSG 2016

Machine Learning Pipeline using Luigi and Scikit Learn - PyConSG 2016

Image from music2vec: A smart music recommendation and discovery - PyConSG 2016

music2vec: A smart music recommendation and discovery - PyConSG 2016

Image from Python in the MicroServices world - PyConSG 2016

Python in the MicroServices world - PyConSG 2016

Image from Python Pants - A build system for large codebases with multiple dependencies - PyConSG 2016

Python Pants - A build system for large codebases with multiple dependencies - PyConSG 2016

Image from Python Robotics for Education - PyConSG 2016

Python Robotics for Education - PyConSG 2016

Image from Sentiment Analysis, its techniques and applications - PyConSG 2016

Sentiment Analysis, its techniques and applications - PyConSG 2016

Image from Using Artificial Life Simulation to Gain Insights into Contradictory Field Evidence - PyConSG 2016

Using Artificial Life Simulation to Gain Insights into Contradictory Field Evidence - PyConSG 2016

Image from Using Python to Build a GIS Data Pipeline for Rural-Urban Classification - PyConSG 2016

Using Python to Build a GIS Data Pipeline for Rural-Urban Classification - PyConSG 2016

Image from Visualising Machine Learning Models - PyConSG 2016

Visualising Machine Learning Models - PyConSG 2016

Image from Warpdrive, making Python web application deployment magically easy - PyConSG 2016

Warpdrive, making Python web application deployment magically easy - PyConSG 2016

Image from Final Remarks and Conference Close

Final Remarks and Conference Close

Image from PyData Amsterdam 2016

PyData Amsterdam 2016

Image from PyAstro16 - Adam Ginsburg - Pyspeckit: Lessons learned from a spectroscopic toolkit…

PyAstro16 - Adam Ginsburg - Pyspeckit: Lessons learned from a spectroscopic toolkit…

Image from PyAstro16 - Andrew Hearin - Halotools: An open-source python platform for studying galaxy evolution…

PyAstro16 - Andrew Hearin - Halotools: An open-source python platform for studying galaxy evolution…

Image from PyAstro16 - Brett Morris - astroplan: Observation Planning for Astronomers

PyAstro16 - Brett Morris - astroplan: Observation Planning for Astronomers

Image from PyAstro16 - Brigitta Sipocz - The Astropy Project

PyAstro16 - Brigitta Sipocz - The Astropy Project

Image from PyAstro16 - Cameron Hummels - Trident and MISTY: a universal pipeline for generating and sharing…

PyAstro16 - Cameron Hummels - Trident and MISTY: a universal pipeline for generating and sharing…

Image from PyAstro16 - Dan Foreman-Mackey - Tools for probabilistic data analysis in Python

PyAstro16 - Dan Foreman-Mackey - Tools for probabilistic data analysis in Python

Image from PyAstro16 - Elise Jennings - CosmoSIS: modular cosmological parameter estimation

PyAstro16 - Elise Jennings - CosmoSIS: modular cosmological parameter estimation

Image from PyAstro16 - James Turner - Streamlining an IRAF data reduction process Pythonically…

PyAstro16 - James Turner - Streamlining an IRAF data reduction process Pythonically…

Image from PyAstro16 - Kyle Barbary - Julia and Python in Astronomy: Better Together

PyAstro16 - Kyle Barbary - Julia and Python in Astronomy: Better Together

Image from PyAstro16 - Lia Corrales - eblur/dust: a modular python approach for dust extinction and scattering

PyAstro16 - Lia Corrales - eblur/dust: a modular python approach for dust extinction and scattering

Image from PyAstro16 - Lightning Talks 1

PyAstro16 - Lightning Talks 1

Image from PyAstro16 - Lightning Talks 2

PyAstro16 - Lightning Talks 2

Image from PyAstro16 - Matthew Craig - Variable star astronomy with undergrads using astropy

PyAstro16 - Matthew Craig - Variable star astronomy with undergrads using astropy

Image from PyAstro16 - Michele Bannister - Making data pretty and understandable

PyAstro16 - Michele Bannister - Making data pretty and understandable

Image from PyAstro16 - Nathan Goldbaum - Publicly Releasing a Large Simulation Dataset with NDS Labs

PyAstro16 - Nathan Goldbaum - Publicly Releasing a Large Simulation Dataset with NDS Labs

Image from PyAstro16 - Sprint/Hack demos

PyAstro16 - Sprint/Hack demos

Image from PyAstro16 - Stephanie Douglas - An inquiry-based programming lesson

PyAstro16 - Stephanie Douglas - An inquiry-based programming lesson

Image from PyAstro16 - Steven Christe - SunPy, the solar data analysis package

PyAstro16 - Steven Christe - SunPy, the solar data analysis package

Image from PyCon PH 2016 - Opening Remarks

PyCon PH 2016 - Opening Remarks

Image from PyCaribbean 2016 Sponsors Video

PyCaribbean 2016 Sponsors Video

Image from Alex Rubinsteyn: Python Libraries for Deep Learning with Sequences

Alex Rubinsteyn: Python Libraries for Deep Learning with Sequences

Image from Andre Panisson: Exploring temporal graph data with Python

Andre Panisson: Exploring temporal graph data with Python

Image from Andreas Mueller, Brian Granger, Jeff Reback, Michael Droettboom (Keynote): New Project Features

Andreas Mueller, Brian Granger, Jeff Reback, Michael Droettboom (Keynote): New Project Features

Image from Andreas Mueller: Machine Learning with scikit learn

Andreas Mueller: Machine Learning with scikit learn

Image from Andrew Campbell: Bootstrapping Applications and Dashboards with IPython Widgets

Andrew Campbell: Bootstrapping Applications and Dashboards with IPython Widgets

Image from Andrew Montalenti: Beating Python's GIL to Max Out Your CPUs

Andrew Montalenti: Beating Python's GIL to Max Out Your CPUs

Image from Andrew Osheroff, Jeremy Freeman, Kyle Kelley: Binder: sharing and reproducing computation

Andrew Osheroff, Jeremy Freeman, Kyle Kelley: Binder: sharing and reproducing computation

Image from Anna Nicanorova: Optimizing Life Everyday Problems Solved with Linear Programing in Python

Anna Nicanorova: Optimizing Life Everyday Problems Solved with Linear Programing in Python

Image from Ben Fields: Great Taste, Less Wordy: Document Summarization of Beer Reviews

Ben Fields: Great Taste, Less Wordy: Document Summarization of Beer Reviews

Image from Brett Cannon: A survey of Python interpreters

Brett Cannon: A survey of Python interpreters

Image from Brian Coffey: D3 in Jupyter

Brian Coffey: D3 in Jupyter

Image from Brian Granger: All About Jupyter

Brian Granger: All About Jupyter

Image from Brian Kent: Density Based Clustering in Python

Brian Kent: Density Based Clustering in Python

Image from Bruno Goncalves, Anastasios Noulas: Mining Georeferenced Data

Bruno Goncalves, Anastasios Noulas: Mining Georeferenced Data

Image from Dan Blanchard: Simplifying large scale parallel processing with Storm and streamparse

Dan Blanchard: Simplifying large scale parallel processing with Storm and streamparse

Image from Daniel Rodriguez: Querying 1 6 billion reddit comments with python

Daniel Rodriguez: Querying 1 6 billion reddit comments with python

Image from Dr Jessica Stauth: Portfolio and Risk Analytics in Python with pyfolio

Dr Jessica Stauth: Portfolio and Risk Analytics in Python with pyfolio

Image from Eben Olson: Neural networks with Theano and Lasagne

Eben Olson: Neural networks with Theano and Lasagne

Image from Eric J Ma: Network Analysis Fundamentals

Eric J Ma: Network Analysis Fundamentals

Image from Francois Scharffe: Word embeddings as a service

Francois Scharffe: Word embeddings as a service

Image from James Powell: Does Code Quality Really Matter

James Powell: Does Code Quality Really Matter

Image from Jeff Reback: Performance Pandas (PyData NYC 2015)

Jeff Reback: Performance Pandas (PyData NYC 2015)

Image from Jessica Forde: Visualizing Wireless Router Timeseries Data with the Density API, Seaborn, and Pandas

Jessica Forde: Visualizing Wireless Router Timeseries Data with the Density API, Seaborn, and Pandas

Image from Jim Crist: Dask Parallelizing NumPy and Pandas through Task Scheduling

Jim Crist: Dask Parallelizing NumPy and Pandas through Task Scheduling

Image from Karim Chine: Towards a universal platform for data science on public and private clouds

Karim Chine: Towards a universal platform for data science on public and private clouds

Image from Karthik Ananth: Scrapy Workshop

Karthik Ananth: Scrapy Workshop

Image from Katrina Riehl (Keynote): Memex: Mining the Dark Web

Katrina Riehl (Keynote): Memex: Mining the Dark Web

Image from Lightning Talks

Lightning Talks

Image from Marcos Vanetta: Reproducibility of your development environment

Marcos Vanetta: Reproducibility of your development environment

Image from Maria Nattestad: How Big Data is transforming biology and how we are using Python to make sense

Maria Nattestad: How Big Data is transforming biology and how we are using Python to make sense

Image from Matthew Conlen: Lightning Web First Data Visualization in Python

Matthew Conlen: Lightning Web First Data Visualization in Python

Image from Michael Droettboom: matplotlib

Michael Droettboom: matplotlib

Image from Mike Mull: The Art and Science of Data Matching

Mike Mull: The Art and Science of Data Matching

Image from Milos Miljkovic: Song Matching by Analyzing and Hashing Audio Fingerprints

Milos Miljkovic: Song Matching by Analyzing and Hashing Audio Fingerprints

Image from Nick Evans: Realtime Risk Management Using Kafka, Python, and Spark Streaming

Nick Evans: Realtime Risk Management Using Kafka, Python, and Spark Streaming

Image from Pawel Potacki: Building interactive visualization with VTK, Matplotlib and Enaml

Pawel Potacki: Building interactive visualization with VTK, Matplotlib and Enaml

Image from Perry Greenfield (Keynote): How Python Found its way Into Astronomy

Perry Greenfield (Keynote): How Python Found its way Into Astronomy

Image from Phillip Cloud: Blaze- An Interface to all the Things

Phillip Cloud: Blaze- An Interface to all the Things

Image from Rajat Arya, Brian Kent: Getting Started with Machine Learning Applications

Rajat Arya, Brian Kent: Getting Started with Machine Learning Applications

Image from Riley H: Social Science Conscious Analysis Case Study: The Cost of Public School In NYC

Riley H: Social Science Conscious Analysis Case Study: The Cost of Public School In NYC

Image from Ryan J. O'Neil: Optimize your Docker Infrastructure with Python

Ryan J. O'Neil: Optimize your Docker Infrastructure with Python

Image from Scott Sanderson: Developing an Expression Language for Quantitative Financial Modeling

Scott Sanderson: Developing an Expression Language for Quantitative Financial Modeling

Image from Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models

Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models

Image from Steve Dower: Using Python in Visual Studio

Steve Dower: Using Python in Visual Studio

Image from Steve Taylor: Correlation Matrix Filtering and Asset Allocation with Python

Steve Taylor: Correlation Matrix Filtering and Asset Allocation with Python

Image from Stuart Williams: Python Epiphanies

Stuart Williams: Python Epiphanies

Image from The Future of Machine Learning Panel Discussion

The Future of Machine Learning Panel Discussion

Image from Timothy Hopper: Understanding Probabilistic Topic Models By Simulation

Timothy Hopper: Understanding Probabilistic Topic Models By Simulation

Image from TJ Torres: Deep Style

TJ Torres: Deep Style

Image from Travis Oliphant & Peter Wang (Keynote): Python as the Zen of Data Science

Travis Oliphant & Peter Wang (Keynote): Python as the Zen of Data Science

Image from Trent Oliphant: Using Django as a data tool in the Enterprise

Trent Oliphant: Using Django as a data tool in the Enterprise

Image from Welcome and quick introduction of all talks

Welcome and quick introduction of all talks

Image from PyconJP2015ポスターセッション

PyconJP2015ポスターセッション

Image from PyconJP2015スポンサーブース

PyconJP2015スポンサーブース

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360view pyconJP2015

Image from CH13 セカイノカオ by チーム・カオ

CH13 セカイノカオ by チーム・カオ

Image from CH14 PyPIデビュー 2015

CH14 PyPIデビュー 2015

Image from CH15 Lightning Talks ~ Closing

CH15 Lightning Talks ~ Closing

Image from CR10 野球Hack!~Pythonを用いたデータ分析と可視化

CR10 野球Hack!~Pythonを用いたデータ分析と可視化

Image from CR11 Rise of the Static Site Generator

CR11 Rise of the Static Site Generator

Image from MH10 Translation of Python Programs into non-English Languages for Learners without English...

MH10 Translation of Python Programs into non-English Languages for Learners without English...

Image from CH07 PyLadies Tokyo－初心者女性向けPython体験ワークショップ開催の裏側

CH07 PyLadies Tokyo－初心者女性向けPython体験ワークショップ開催の裏側

Image from CH08 Panel Discussion - いま求められるコミュニティの多様性と未来

CH08 Panel Discussion - いま求められるコミュニティの多様性と未来

Image from CH09 Lightning Talks ~ Closing

CH09 Lightning Talks ~ Closing

Image from CH10 Keynote

CH10 Keynote

Image from CH11 pandas internals

CH11 pandas internals

Image from CH12 Python で作って学ぶ形態素解析

CH12 Python で作って学ぶ形態素解析

Image from CR06 tse - Pythonによるテキスト整形ユーティリティ

CR06 tse - Pythonによるテキスト整形ユーティリティ

Image from CR07 Teaching Data Structures with Python

CR07 Teaching Data Structures with Python

Image from CR08 Pythonで3Dデータモデリング&3Dプリント

CR08 Pythonで3Dデータモデリング&3Dプリント

Image from CR09 Writing Fast Code

CR09 Writing Fast Code

Image from MH06 Let it crash: what Python can learn from Erlang.

MH06 Let it crash: what Python can learn from Erlang.

Image from MH07 Packaging最前線

MH07 Packaging最前線

Image from MH08 PythonとPyCoRAMでお手軽にFPGAシステムを開発してみよう

MH08 PythonとPyCoRAMでお手軽にFPGAシステムを開発してみよう

Image from MH09 uWSGI/Dockerを利用したWebサービス運用事例

MH09 uWSGI/Dockerを利用したWebサービス運用事例

Image from MH11 アドネットワークのデータ解析チームを支える技術

MH11 アドネットワークのデータ解析チームを支える技術

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CH01 Opening〜Keynote

Image from CH02 強くなるためのプログラミングープログラミングに関する様々なコンテストとそのはじめ方ー

CH02 強くなるためのプログラミングープログラミングに関する様々なコンテストとそのはじめ方ー

Image from CH03 How we realize SOA by Python

CH03 How we realize SOA by Python

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CH04 日本のオープンデータプラットフォームをPythonでつくる

Image from CH05 Tornado/ElasticSearchで実現する大量ツイートのリアルタイム関連文書判定

CH05 Tornado/ElasticSearchで実現する大量ツイートのリアルタイム関連文書判定

Image from CH06 Python and the Semantic Web: Building a Linked Data Fragment Server with Asynco and Redis

CH06 Python and the Semantic Web: Building a Linked Data Fragment Server with Asynco and Redis

Image from CR01 組合せ最適化を体系的に知ってPythonで実行してみよう

CR01 組合せ最適化を体系的に知ってPythonで実行してみよう

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CR02 Pythonによる4足歩行ロボットの制御と強化学習による歩行動作獲得の実例

Image from CR03 なぜWeb画面自動テストはうまく行かないのか

CR03 なぜWeb画面自動テストはうまく行かないのか

Image from CR04 Karaoke-style Read-aloud System

CR04 Karaoke-style Read-aloud System

Image from CR05 Sphinxで作る貢献しやすいドキュメント翻訳の仕組み

CR05 Sphinxで作る貢献しやすいドキュメント翻訳の仕組み

Image from MH01 Ways to avoid overfitting when developing a trading strategy

MH01 Ways to avoid overfitting when developing a trading strategy

Image from MH02 Python と型ヒント (Type Hints)

MH02 Python と型ヒント (Type Hints)

Image from MH03 Building a Scalable Python gRPC Service using Kubernetes

MH03 Building a Scalable Python gRPC Service using Kubernetes

Image from MH04 MinecraftをPythonで遊ぶ

MH04 MinecraftをPythonで遊ぶ

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MH05 Pythonで作る俺様サウンドエフェクタ

Image from R0 DAY2 Bingo Game (PyCon APAC 2015)

R0 DAY2 Bingo Game (PyCon APAC 2015)

Image from PyCon PH 2015 - Community Panel

PyCon PH 2015 - Community Panel

Image from PyCon PH 2015 - Intro

PyCon PH 2015 - Intro

Image from Alejandro Correa Bahnsen: CostCla a cost-sensitive classification library

Alejandro Correa Bahnsen: CostCla a cost-sensitive classification library

Image from Brian Carter: Lifecycle of Web Text Mining: Scrape to Sense

Brian Carter: Lifecycle of Web Text Mining: Scrape to Sense

Image from Felix Wick: From the Life of a Data Scientist

Felix Wick: From the Life of a Data Scientist

Image from Ignacio Elola: Python as a Framework for Analytics and Growth Hacking

Ignacio Elola: Python as a Framework for Analytics and Growth Hacking

Image from Jose Luis Lopez Pino: Lessons learned from applying PyData to our marketing organization

Jose Luis Lopez Pino: Lessons learned from applying PyData to our marketing organization

Image from Lea Böhm: What people need to be happy at work & how you can influence a diverse team environment

Lea Böhm: What people need to be happy at work & how you can influence a diverse team environment

Image from Miguel Fernando Cabrera: Processing Hotel Reviews with Python

Miguel Fernando Cabrera: Processing Hotel Reviews with Python

Image from Panel Discussion

Panel Discussion

Image from Peter Hoffmann: Indroduction to the PySpark DataFrame API

Peter Hoffmann: Indroduction to the PySpark DataFrame API

Image from Philipp Pahl: Peachbox: Agile and Accessible Big ETL Framework

Philipp Pahl: Peachbox: Agile and Accessible Big ETL Framework

Image from Sylvain Bellemare: Learning to use Docker for development

Sylvain Bellemare: Learning to use Docker for development

Image from Thomas Pfaff: Advanced Data Storage

Thomas Pfaff: Advanced Data Storage

Image from Trent McConaghy: Rewiring the Internet for Ownership with Big Data and Blockchains

Trent McConaghy: Rewiring the Internet for Ownership with Big Data and Blockchains

Image from Alexander Kagoshima: A Data Science Operationalization Framework

Alexander Kagoshima: A Data Science Operationalization Framework

Image from Christine Doig: Interactive Data Visualizations with Python

Christine Doig: Interactive Data Visualizations with Python

Image from Claas Abert: Scientific computing with Python: Tools for the solution of continuous problems

Claas Abert: Scientific computing with Python: Tools for the solution of continuous problems

Image from Matthew Rocklin: pyData Berlin 2015: keynote

Matthew Rocklin: pyData Berlin 2015: keynote

Image from Mike Müller: Measure, don't Guess - How to find out if and where to optimize

Mike Müller: Measure, don't Guess - How to find out if and where to optimize

Image from Paul Balzer: Analysing and predicting inner-city parking space occupancy

Paul Balzer: Analysing and predicting inner-city parking space occupancy

Image from Paul Balzer: Running, walking, sitting or biking? - Motion prediction with acceleration and rotation

Paul Balzer: Running, walking, sitting or biking? - Motion prediction with acceleration and rotation

Image from Ronert Obst: Smart cars of tomorrow: real-time driving patterns

Ronert Obst: Smart cars of tomorrow: real-time driving patterns

Image from The meta API by Daniel Pyrathon 1

The meta API by Daniel Pyrathon 1

Image from Tobias Kuhn, Nakul Selvaraj: Real-Time Monitoring of Distributed Systems

Tobias Kuhn, Nakul Selvaraj: Real-Time Monitoring of Distributed Systems

Image from Valentin Haenel: Blosc

Valentin Haenel: Blosc

Image from Various: Lightning Talks

Various: Lightning Talks

Image from Andy R. Terrel and Christine Doig - Building Python Data Applications with Blaze and Bokeh

Andy R. Terrel and Christine Doig - Building Python Data Applications with Blaze and Bokeh

Image from Andy R. Terrel - Python in Scientific Research

Andy R. Terrel - Python in Scientific Research

Image from Chou-han Yang - Briefly - A Python DSL to Scale Complex Mapreduce Pipelines

Chou-han Yang - Briefly - A Python DSL to Scale Complex Mapreduce Pipelines

Image from David Perez Suarez - Recipy

David Perez Suarez - Recipy

Image from Jeb Stone, Jigar Mistry and Rohan Patil - Social Media Analysis in Python

Jeb Stone, Jigar Mistry and Rohan Patil - Social Media Analysis in Python

Image from Lightning Talks 1

Lightning Talks 1

Image from Lightning Talks 2

Lightning Talks 2

Image from Lightning Talks 3

Lightning Talks 3

Image from Rajat Arya and Yucheng Low - Building Machine Learning Applications in Python

Rajat Arya and Yucheng Low - Building Machine Learning Applications in Python

Image from Mejores Momentos

Mejores Momentos

Image from Alain Ledon & Amit Bhattacharyya - Logistics Regression & NFL

Alain Ledon & Amit Bhattacharyya - Logistics Regression & NFL

Image from Bryan Van De Ven - Beautiful Interactive Visualizations in the Browser with Bokeh

Bryan Van De Ven - Beautiful Interactive Visualizations in the Browser with Bokeh

Image from Luis Miguel Sanchez - Using Python to Design a Parametric Catastrophic CAT Earthquake Bond

Luis Miguel Sanchez - Using Python to Design a Parametric Catastrophic CAT Earthquake Bond

Image from Matt Greenwood & Scott Draves - The Polyglot Beaker Notebook

Matt Greenwood & Scott Draves - The Polyglot Beaker Notebook

Image from Async Programming and Python

Async Programming and Python

Image from Asyncio for Dummies

Asyncio for Dummies

Image from Auth as a service with Lastuser

Auth as a service with Lastuser

Image from Building highly scalable web services with Gevent experiences at Plivo

Building highly scalable web services with Gevent experiences at Plivo

Image from Day 1 Lightning Talks

Day 1 Lightning Talks

Image from Development to Production

Development to Production

Image from Django Design Patterns

Django Design Patterns

Image from Faster data processing in Python

Faster data processing in Python

Image from GPU accelerated high performance computing primer

GPU accelerated high performance computing primer

Image from Introduction to BinPy An Electronic Simulation Library

Introduction to BinPy An Electronic Simulation Library

Image from Key Note Michael Foord

Key Note Michael Foord

Image from Medusa A much faster Python implementation based on the Dart Virtual Machine

Medusa A much faster Python implementation based on the Dart Virtual Machine

Image from Meet ElasticSearch My Pet Crunch Monster @Directi

Meet ElasticSearch My Pet Crunch Monster @Directi

Image from Messing with government data using Python

Messing with government data using Python

Image from Neo4J and Python Playing with graph data

Neo4J and Python Playing with graph data

Image from New Scientific Plotting in Python

New Scientific Plotting in Python

Image from Panel Discussion Flask vs Django vs web py

Panel Discussion Flask vs Django vs web py

Image from Python, Arduino and connected objects

Python, Arduino and connected objects

Image from Python + Spark Lightning Fast Cluster Computing

Python + Spark Lightning Fast Cluster Computing

Image from Python, the eyes of real world computer applications

Python, the eyes of real world computer applications

Image from SHARQ An API queueing system built at Plivo!

SHARQ An API queueing system built at Plivo!

Image from Transfer Pipeline at DreamWorks

Transfer Pipeline at DreamWorks

Image from Which messaging layer should you use if you want to build a loosely coupled distributed Python app

Which messaging layer should you use if you want to build a loosely coupled distributed Python app

Image from CH14 データ分析の世界へようこそ！～マーケティングに活かせるPythonライブラリ～ (ja)

CH14 データ分析の世界へようこそ！～マーケティングに活かせるPythonライブラリ～ (ja)

Image from CH15 Pythonではじめる野球プログラミング (ja)

CH15 Pythonではじめる野球プログラミング (ja)

Image from CH16 Lightning Talks ~ Closing

CH16 Lightning Talks ~ Closing

Image from CR12 WebSocket 101 in Python (en)

CR12 WebSocket 101 in Python (en)

Image from CR13 Python Quest in Taiwan (en)

CR13 Python Quest in Taiwan (en)

Image from MH12 パッケージングの今 (ja)

MH12 パッケージングの今 (ja)

Image from MH13 正規表現リテラルは本当に必要なのか？ (ja)

MH13 正規表現リテラルは本当に必要なのか？ (ja)

Image from CH07 リファクタリングツールあれこれ (May the force be with you) (ja)

CH07 リファクタリングツールあれこれ (May the force be with you) (ja)

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CH08 Python を支える技術: ディスクリプタ編 (ja)

Image from CH09 Lightning Talks

CH09 Lightning Talks

Image from CH10 Opening~Keynote: Hirokazu Nishio

CH10 Opening~Keynote: Hirokazu Nishio

Image from CH11 Oktavia - Search Engine (en)

CH11 Oktavia - Search Engine (en)

Image from CH12 PyNES: Python programming for Nintendo 8 bits. (en)

CH12 PyNES: Python programming for Nintendo 8 bits. (en)

Image from CH13 Improving code quality through static analysis for Python (en)

CH13 Improving code quality through static analysis for Python (en)

Image from CR06 Pelicanによる静的ウェブサイトの構築 - www.python.jp サイトの仕組みと構造 (ja)

CR06 Pelicanによる静的ウェブサイトの構築 - www.python.jp サイトの仕組みと構造 (ja)

Image from CR07 Pyramid入門 (ja)

CR07 Pyramid入門 (ja)

Image from CR09 OpenCVのpythonインターフェース入門 (ja)

CR09 OpenCVのpythonインターフェース入門 (ja)

Image from CR10 Pythonとscikit-learnではじめる機械学習 (ja)

CR10 Pythonとscikit-learnではじめる機械学習 (ja)

Image from CR11 数理最適化によるパズルの解法 (ja)

CR11 数理最適化によるパズルの解法 (ja)

Image from MH06 Open Data for Taiwan's Roadways (en)

MH06 Open Data for Taiwan's Roadways (en)

Image from MH07 Mining sentiments in tweets using Python (en)

MH07 Mining sentiments in tweets using Python (en)

Image from MH09

MH09 "Continuous Publication" with Python: Another Approach (ja)

Image from MH10 Pythonによる非同期プログラミング入門 (ja)

MH10 Pythonによる非同期プログラミング入門 (ja)

Image from MH11 Python + Hive on AWS EMRで貧者のログ集計 (ja)

MH11 Python + Hive on AWS EMRで貧者のログ集計 (ja)

Image from CH01 Opening~Keynote: Kenneth Reitz

CH01 Opening~Keynote: Kenneth Reitz

Image from CH02 Djangoによるスマホアプリバックエンドの実装 (ja)

CH02 Djangoによるスマホアプリバックエンドの実装 (ja)

Image from CH03 Djangoアプリケーション、パフォーマンスチューニング (ja)

CH03 Djangoアプリケーション、パフォーマンスチューニング (ja)

Image from CH04 Pythonの実装系総ざらい (ja)

CH04 Pythonの実装系総ざらい (ja)

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CH05 最新リリースCMSツール Plone 5 のモダンUIとテクノロジーの進化 (ja)

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CH06 PyCharm活用術 (ja)

Image from CR01 Programming the Performance Co-Pilot toolkit (en)

CR01 Programming the Performance Co-Pilot toolkit (en)

Image from CR02 Gunicorn, the thundering herd and other concurrency programming challenges (en)

CR02 Gunicorn, the thundering herd and other concurrency programming challenges (en)

Image from CR03 Python, Raspberry Pi, Arduinoで作る消費電力モニタリングシステム (ja)

CR03 Python, Raspberry Pi, Arduinoで作る消費電力モニタリングシステム (ja)

Image from CR04 Micro Python で組み込み Python (ja)

CR04 Micro Python で組み込み Python (ja)

Image from CR05 XML-RPC : Pythonが「電池付属」と呼ばれる理由 (ja)

CR05 XML-RPC : Pythonが「電池付属」と呼ばれる理由 (ja)

Image from MH01 Deep Learning for Image Recognition in Python (ja)

MH01 Deep Learning for Image Recognition in Python (ja)

Image from MH02 The esperanto generator (ja)

MH02 The esperanto generator (ja)

Image from MH03 Data collection, analysis and optimization with python (en)

MH03 Data collection, analysis and optimization with python (en)

Image from MH04 Effective numerical computation in Numpy and Scipy (en)

MH04 Effective numerical computation in Numpy and Scipy (en)

Image from MH05 Introduction to scientific programming in python (en)

MH05 Introduction to scientific programming in python (en)

Image from PYCONポスターセッション

PYCONポスターセッション

Image from PYCONスポンサーブース

PYCONスポンサーブース

Image from Conference Closing

Conference Closing

Image from Pycon Cinque Reportage

Pycon Cinque Reportage

Image from Alex Bokov & Dan Connolly - Using Python and Paver to Control a Large Medical Informatics

Alex Bokov & Dan Connolly - Using Python and Paver to Control a Large Medical Informatics

Image from Andrew Montalenti & Keith Bourgoin - Real-time streams and logs with Storm and Kafka

Andrew Montalenti & Keith Bourgoin - Real-time streams and logs with Storm and Kafka

Image from Brian Granger and Jonathan Frederic - IPython Interactive Widgets, Part 1

Brian Granger and Jonathan Frederic - IPython Interactive Widgets, Part 1

Image from Brian Granger and Jonathan Frederic - IPython Interactive Widgets, Part 2

Brian Granger and Jonathan Frederic - IPython Interactive Widgets, Part 2

Image from Brian Granger and Jonathan Frederic - IPython Interactive Widgets, Part 3

Brian Granger and Jonathan Frederic - IPython Interactive Widgets, Part 3

Image from Paul Ivanov and Thomas Kluyer - The IPython Protocol, Frontends and Kernels

Paul Ivanov and Thomas Kluyer - The IPython Protocol, Frontends and Kernels

Image from Closing address - PyCon 2014 (2014/04/13)

Closing address - PyCon 2014 (2014/04/13)

Image from PyConES 2013. Así fue...

PyConES 2013. Así fue...

Image from Machine learning, de BityVip

Machine learning, de BityVip

Image from Panel discussion: Effective Team Practices in Software Development (part 2)

Panel discussion: Effective Team Practices in Software Development (part 2)

Image from DjangoCon Intro and Keynote: State of the Pony Report -- Russell Keith-Magee

DjangoCon Intro and Keynote: State of the Pony Report -- Russell Keith-Magee

Image from Python In Education (Panel Discussion)

Python In Education (Panel Discussion)

Image from Conference Close

Conference Close

Image from Conference Opening & Housekeeping

Conference Opening & Housekeeping

Image from Core Developer Panel

Core Developer Panel

Image from EuroPython 2013 - Reportage Teaser (Harlem Shake)

EuroPython 2013 - Reportage Teaser (Harlem Shake)

Image from A Rapidly-Adaptable Imaging & Measurement Platform for Cancer Research; SciPy 2013 Presentation

A Rapidly-Adaptable Imaging & Measurement Platform for Cancer Research; SciPy 2013 Presentation

Image from Accessing the Virtual Observatory from Python; SciPy 2013 Presentation

Accessing the Virtual Observatory from Python; SciPy 2013 Presentation

Image from Advances in delivery and access tools for coastal ocean model data; SciPy 2013 Presentation

Advances in delivery and access tools for coastal ocean model data; SciPy 2013 Presentation

Image from An Open Source System for De-identification and Use of Medical Images; SciPy 2013 Presentation

An Open Source System for De-identification and Use of Medical Images; SciPy 2013 Presentation

Image from Analyzing IBM Watson experiments with IPython Notebook; SciPy 2013 Presentation

Analyzing IBM Watson experiments with IPython Notebook; SciPy 2013 Presentation

Image from Automating Quantitative Confocal Microscopy Analysis; SciPy 2013 Presentation

Automating Quantitative Confocal Microscopy Analysis; SciPy 2013 Presentation

$Image from Breaking the diffraction limit with python and scipy; SciPy 2013 Presentation$

Breaking the diffraction limit with python and scipy; SciPy 2013 Presentation

Image from Bringing astronomical tools down to earth; SciPy 2013 Presentation

Bringing astronomical tools down to earth; SciPy 2013 Presentation

Image from Climate Observations from ACIS in pandas; SciPy 2013 Presentation

Climate Observations from ACIS in pandas; SciPy 2013 Presentation

Image from Data Agnosticism: Feature Engineering Without Domain Expertise; SciPy 2013 Presentation

Data Agnosticism: Feature Engineering Without Domain Expertise; SciPy 2013 Presentation

Image from DMTCP: Bringing Checkpoint-Restart to Python; SciPy 2013 Presentation

DMTCP: Bringing Checkpoint-Restart to Python; SciPy 2013 Presentation

Image from Dynamics with SymPy Mechanics; SciPy 2013 Presentation

Dynamics with SymPy Mechanics; SciPy 2013 Presentation

Image from Estimating and Visualizing the Inertia of the Human Body with Python; SciPy 2013 Presentation

Estimating and Visualizing the Inertia of the Human Body with Python; SciPy 2013 Presentation

Image from Experiences in Python for Medical Image Analysis; SciPy 2013 Presentation

Experiences in Python for Medical Image Analysis; SciPy 2013 Presentation

Image from Exploring Collaborative HPC Visualization Workflows using VisIt and Python; SciPy 2013 Presentation

Exploring Collaborative HPC Visualization Workflows using VisIt and Python; SciPy 2013 Presentation

Image from High Performance Reproducible Computing; SciPy 2013 Presentation

High Performance Reproducible Computing; SciPy 2013 Presentation

Image from Iris & Cartopy: Python packages for Atmospheric and Oceanographic science; SciPy 2013 Presentation

Iris & Cartopy: Python packages for Atmospheric and Oceanographic science; SciPy 2013 Presentation

Image from lmonade: a platform for development and distribution of scientific software; SciPy 2013 Presentation

lmonade: a platform for development and distribution of scientific software; SciPy 2013 Presentation

Image from Massive Online Collaborative Research and Modeling using Synapse and Python; SciPy 2013 Presentation

Massive Online Collaborative Research and Modeling using Synapse and Python; SciPy 2013 Presentation

Image from Matplotlib: past, present and future; SciPy 2013 Presentation

Matplotlib: past, present and future; SciPy 2013 Presentation

Image from Matrix Expressions and BLAS/LAPACK; SciPy 2013 Presentation

Matrix Expressions and BLAS/LAPACK; SciPy 2013 Presentation

Image from NeuroTrends: Large-scale automated analysis of the neuroimaging literature; SciPy 2013 Presentation

NeuroTrends: Large-scale automated analysis of the neuroimaging literature; SciPy 2013 Presentation

Image from Oil spill modeling and uncertainty forecasts with Python; SciPy 2013 Presentation

Oil spill modeling and uncertainty forecasts with Python; SciPy 2013 Presentation

Image from open('/dev/real_world') - Raspberry Pi Sensor and Actuator Control; SciPy 2013 Presentation

open('/dev/real_world') - Raspberry Pi Sensor and Actuator Control; SciPy 2013 Presentation

Image from Opening Up Astronomy with Python and AstroML; SciPy 2013 Presentation

Opening Up Astronomy with Python and AstroML; SciPy 2013 Presentation

Image from Parallel Volume Rendering in yt: User Driven & User Developed; SciPy 2013 Presentation

Parallel Volume Rendering in yt: User Driven & User Developed; SciPy 2013 Presentation

Image from Python Tools for Coding and Feature Learning; SciPy 2013 Presentation

Python Tools for Coding and Feature Learning; SciPy 2013 Presentation

Image from Reproducible Documents with PythonTeX; SciPy 2013 Presentation

Reproducible Documents with PythonTeX; SciPy 2013 Presentation

Image from Scikit-Fuzzy: A New SciPy Toolkit for Fuzzy Logic; SciPy 2013 Presentation

Scikit-Fuzzy: A New SciPy Toolkit for Fuzzy Logic; SciPy 2013 Presentation

Image from SciPy 2013 John Hunter Excellence in Plotting Contest

SciPy 2013 John Hunter Excellence in Plotting Contest

Image from SymPy Gamma and SymPy Live: Python and Mathematics Online; SciPy 2013 Presentation

SymPy Gamma and SymPy Live: Python and Mathematics Online; SciPy 2013 Presentation

Image from The DyND Library; SciPy 2013 Presentation

The DyND Library; SciPy 2013 Presentation

Image from The Open Science Framework: Improving, by Opening, Science; SciPy 2013 Presentation

The Open Science Framework: Improving, by Opening, Science; SciPy 2013 Presentation

Image from Using IPython Notebook with IPython Cluster; SciPy 2013 Presentation

Using IPython Notebook with IPython Cluster; SciPy 2013 Presentation

Image from Using Python to drive the General NOAA Operational Modeling Environment; SciPy 2013 Presentation

Using Python to drive the General NOAA Operational Modeling Environment; SciPy 2013 Presentation

Image from XDress - Type, But Verify; SciPy 2013 Presentation

XDress - Type, But Verify; SciPy 2013 Presentation

Image from A Gentle Introduction To Machine Learning; SciPy 2013 Presentation

A Gentle Introduction To Machine Learning; SciPy 2013 Presentation

Image from An efficient workflow for reproducible science; SciPy 2013 Presentation

An efficient workflow for reproducible science; SciPy 2013 Presentation

Image from Astropy, growing a community-based software system for astronomy; SciPy 2013 Presentation

Astropy, growing a community-based software system for astronomy; SciPy 2013 Presentation

Image from Best-practice variant calling pipeline for automated sequencing analysis; SciPy 2013 Presentation

Best-practice variant calling pipeline for automated sequencing analysis; SciPy 2013 Presentation

Image from Combining C++ and Python in the LSST Software Stack; SciPy 2013 Presentation

Combining C++ and Python in the LSST Software Stack; SciPy 2013 Presentation

Image from Complex Experiment Configuration ... using Robot Operating System (ROS); SciPy 2013 Presentation

Complex Experiment Configuration ... using Robot Operating System (ROS); SciPy 2013 Presentation

Image from Exploring disease genetics from thousands of individual genomes with Gemini; SciPy 2013 Presentation

Exploring disease genetics from thousands of individual genomes with Gemini; SciPy 2013 Presentation

Image from Ginga: an open-source astronomical image viewer and toolkit; SciPy 2013 Presentation

Ginga: an open-source astronomical image viewer and toolkit; SciPy 2013 Presentation

Image from GIS Panel Discussion; SciPy 2013 Presentation

GIS Panel Discussion; SciPy 2013 Presentation

Image from GraphTerm: A notebook-like graphical terminal interface; SciPy 2013 Presentation

GraphTerm: A notebook-like graphical terminal interface; SciPy 2013 Presentation

Image from Hyperopt: A Python library for optimizing machine learning algorithms; SciPy 2013

Hyperopt: A Python library for optimizing machine learning algorithms; SciPy 2013

Image from Import without a filesystem, SciPy 2013 Presentation

Import without a filesystem, SciPy 2013 Presentation

Image from Infer.py: Probabilistic Programming and Bayesian Inference from Python; SciPy 2013 Presentation

Infer.py: Probabilistic Programming and Bayesian Inference from Python; SciPy 2013 Presentation

Image from IPython-powered Slideshow Reveal-ed; SciPy 2013 Presentation

IPython-powered Slideshow Reveal-ed; SciPy 2013 Presentation

Image from Julia and Python: a dynamic duo for scientific computing; SciPy 2013 Presentation

Julia and Python: a dynamic duo for scientific computing; SciPy 2013 Presentation

Image from LarvaMap - A python powered larval transport modeling system; SciPy 2013 Presentation

LarvaMap - A python powered larval transport modeling system; SciPy 2013 Presentation

Image from lpEdit: An editor to facilitate reproducible analysis; SciPy 2013 Presentation

lpEdit: An editor to facilitate reproducible analysis; SciPy 2013 Presentation

Image from metaseq: a Python framework for integrating sequencing analyses; SciPy 2013 Presentation

metaseq: a Python framework for integrating sequencing analyses; SciPy 2013 Presentation

Image from MIST: Micro-Simulation Tool to Support Disease Modeling; SciPy 2013 Presentation

MIST: Micro-Simulation Tool to Support Disease Modeling; SciPy 2013 Presentation

Image from Modeling the Earth with Fatiando a Terra; SciPy 2013 Presentation

Modeling the Earth with Fatiando a Terra; SciPy 2013 Presentation

Image from Multidimensional Data Exploration with Glue; SciPy 2013 Presentation

Multidimensional Data Exploration with Glue; SciPy 2013 Presentation

Image from Mystic: a framework for predictive science; SciPy 2013 Presentation

Mystic: a framework for predictive science; SciPy 2013 Presentation

Image from Optimizing Geographic Processing and Analysis for Big Data; SciPy 2013 Presentation

Optimizing Geographic Processing and Analysis for Big Data; SciPy 2013 Presentation

Image from OS deduplication with SIDUS (single-instance distributing universal system); SciPy 2013 Presentation

OS deduplication with SIDUS (single-instance distributing universal system); SciPy 2013 Presentation

Image from Processing biggish data on commodity hardware: simple Python patterns; SciPy 2013 Presentation

Processing biggish data on commodity hardware: simple Python patterns; SciPy 2013 Presentation

Image from PyOP2: a Framework for Performance-Portable Unstructured Mesh-based Simulations, SciPy 2013

PyOP2: a Framework for Performance-Portable Unstructured Mesh-based Simulations, SciPy 2013

Image from Python and the SKA; SciPy 2013 Presentation

Python and the SKA; SciPy 2013 Presentation

Image from Pythran: Enabling Static Optimization of Scientific Python Programs; SciPy 2013 Presentation

Pythran: Enabling Static Optimization of Scientific Python Programs; SciPy 2013 Presentation

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Questions to Holger Krekel - The return of peer to peer computing

Image from Roadmap to a Sentience Stack; SciPy 2013 Presentation

Roadmap to a Sentience Stack; SciPy 2013 Presentation

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SCI-WMS: A Python Based Web Map Service For Met-Ocean Data; SciPy 2013 Presentation

Image from Scientific Computing and the Materials Genome Initiative; SciPy 2013 Presentation

Scientific Computing and the Materials Genome Initiative; SciPy 2013 Presentation

Image from SciPy 2013 Conference Welcome and Introduction

SciPy 2013 Conference Welcome and Introduction

Image from SciPy 2013 Keynote: IPython: the method behind the madness

SciPy 2013 Keynote: IPython: the method behind the madness

Image from SciPy 2013 Keynote: The New Scientific Publishers

SciPy 2013 Keynote: The New Scientific Publishers

Image from SciPy 2013 Keynote: Trends in Machine Learning and the SciPy community

SciPy 2013 Keynote: Trends in Machine Learning and the SciPy community

Image from Skdata: Data seets and algorithm evaluation protocols in Python; SciPy 2013 Presentation

Skdata: Data seets and algorithm evaluation protocols in Python; SciPy 2013 Presentation

Image from Stuff to do with your genomic intervals; SciPy 2013 Presentation

Stuff to do with your genomic intervals; SciPy 2013 Presentation

Image from SunPy - Python for Solar Physicists; SciPy 2013 Presentation

SunPy - Python for Solar Physicists; SciPy 2013 Presentation

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The advantages of a scientific IDE; SciPy 2013 Presentation

Image from The Production of a Multi-Resolution Global Index for GIS; SciPy 2013 Presentation

The Production of a Multi-Resolution Global Index for GIS; SciPy 2013 Presentation

Image from Using Python for Structured Prediction; SciPy 2013 Presentation

Using Python for Structured Prediction; SciPy 2013 Presentation

Image from vIPer, a new tool to work with IPython notebooks; SciPy 2013 Presentations

vIPer, a new tool to work with IPython notebooks; SciPy 2013 Presentations

Image from Why you should write buggy software with as few features as possible; SciPy 2013 Presentation

Why you should write buggy software with as few features as possible; SciPy 2013 Presentation

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Writing Reproducible Papers with Dexy; SciPy 2013 Presentation

Image from Anatomy of Matplotlib, SciPy2013 Tutorial, Part 1 of 3

Anatomy of Matplotlib, SciPy2013 Tutorial, Part 1 of 3

Image from Anatomy of Matplotlib, SciPy2013 Tutorial, Part 2 of 3

Anatomy of Matplotlib, SciPy2013 Tutorial, Part 2 of 3

Image from Anatomy of Matplotlib, SciPy2013 Tutorial, Part 3 of 3

Anatomy of Matplotlib, SciPy2013 Tutorial, Part 3 of 3

Image from Cython: Speed up Python and NumPy, Pythonize C, C++, and Fortran, SciPy2013 Tutorial, Part 4 of 4

Cython: Speed up Python and NumPy, Pythonize C, C++, and Fortran, SciPy2013 Tutorial, Part 4 of 4

Image from Cython: Speed up Python and NumPy, Pythonize C, C++, and Fortran, SciPy2013 Tutorial, Part 1 of 4

Cython: Speed up Python and NumPy, Pythonize C, C++, and Fortran, SciPy2013 Tutorial, Part 1 of 4

Image from Cython: Speed up Python and NumPy, Pythonize C, C++, and Fortran, SciPy2013 Tutorial, Part 2 of 4

Cython: Speed up Python and NumPy, Pythonize C, C++, and Fortran, SciPy2013 Tutorial, Part 2 of 4

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Cython: Speed up Python and NumPy, Pythonize C, C++, and Fortran, SciPy2013 Tutorial, Part 3 of 4

Image from Data Processing with Python, SciPy2013 Tutorial, Part 3 of 3

Data Processing with Python, SciPy2013 Tutorial, Part 3 of 3

Image from Data Processing with Python, SciPy2013 Tutorial, Part 2 of 3

Data Processing with Python, SciPy2013 Tutorial, Part 2 of 3

Image from Data Processing with Python, SciPy2013 Tutorial, Part 1 of 3

Data Processing with Python, SciPy2013 Tutorial, Part 1 of 3

Image from Diving into NumPy Code, SciPy2013 Tutorial, Part 4 of 4

Diving into NumPy Code, SciPy2013 Tutorial, Part 4 of 4

Image from Diving into NumPy Code, SciPy2013 Tutorial, Part 1 of 4

Diving into NumPy Code, SciPy2013 Tutorial, Part 1 of 4

Image from Diving into NumPy Code, SciPy2013 Tutorial, Part 2 of 4

Diving into NumPy Code, SciPy2013 Tutorial, Part 2 of 4

Image from Diving into NumPy Code, SciPy2013 Tutorial, Part 3 of 4

Diving into NumPy Code, SciPy2013 Tutorial, Part 3 of 4

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Intro to scikit-learn (I), SciPy2013 Tutorial, Part 3 of 3

Image from Intro to scikit-learn (I), SciPy2013 Tutorial, Part 2 of 3

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 2 of 3

Image from Intro to scikit-learn (I), SciPy2013 Tutorial, Part 1 of 3

Intro to scikit-learn (I), SciPy2013 Tutorial, Part 1 of 3

Image from Intro to scikit-learn (II), SciPy2013 Tutorial, Part 2 of 2

Intro to scikit-learn (II), SciPy2013 Tutorial, Part 2 of 2

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Intro to scikit-learn (II), SciPy2013 Tutorial, Part 1 of 2

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IPython in Depth, SciPy2013 Tutorial, Part 1 of 3

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IPython in Depth, SciPy2013 Tutorial, Part 2 of 3

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IPython in Depth, SciPy2013 Tutorial, Part 3 of 3

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NumPy and IPython, SciPy2013 Tutorial, Part 1 of 2

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NumPy and IPython, SciPy2013 Tutorial, Part 2 of 2

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Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 1 of 4

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Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 4 of 4

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Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 3 of 4

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Statistical Data Analysis in Python, SciPy2013 Tutorial, Part 2 of 4

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Symbolic Computing with SymPy, SciPy2013 Tutorial, Part 5 of 6

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Symbolic Computing with SymPy, SciPy2013 Tutorial, Part 4 of 6

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Symbolic Computing with SymPy, SciPy2013 Tutorial, Part 3 of 6

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Symbolic Computing with SymPy, SciPy2013 Tutorial, Part 6 of 6

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Using Geospatial Data with Python, SciPy2013 Tutorial, Part 1 of 6

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Using Geospatial Data with Python, SciPy2013 Tutorial, Part 6 of 6

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Using Geospatial Data with Python, SciPy2013 Tutorial, Part 5 of 6

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Using Geospatial Data with Python, SciPy2013 Tutorial, Part 4 of 6

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Using Geospatial Data with Python, SciPy2013 Tutorial, Part 3 of 6

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Using Geospatial Data with Python, SciPy2013 Tutorial, Part 2 of 6

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Symbolic Computing with SymPy, SciPy2013 Tutorial, Part 2 of 6

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Guppy, un outil d'analyse de l'espace mémoire de process Python

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Intro to Marmir: Spreadsheets on steroids

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00074

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00075

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00076

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Door Prizes

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Lightning Talk: Mezzanine CMS Framework

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Lightning Talk: YAML

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Lightning Talks - Thursday

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Recruiting session

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EuroPython 2012 - Track Big Mac

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Open Government Data Movement

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Python powered search

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Gloriajw-APracticalGuideToNonblockingIOCoroutinesAndConcurrency489.m4v

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Gloriajw-ArtCulturalConcernAndComputerCode967.ogv

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Gloriajw-BestPracticePanel786.m4v

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Gloriajw-DanCrosta393.m4v

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Gloriajw-DeclarativeUIsWithEnami607.m4v

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Gloriajw-MarkovInPython189.ogv

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Gloriajw-MongoDBAndPythonMathiasStern10gen267.m4v

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Gloriajw-MongoDBSchemaDesignKyleBanker10gen849.m4v

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Safely deploying on the cutting edge

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EuroPython 2011: Video Reportage!

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EuroPython 2012: Join us in Florence on July!

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PyOhio 2011: Python and Entrepreneurship

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PL/Python -- Python inside the PostgreSQL RDBMS

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Unladen Swallow: fewer coconuts, faster Python (#73)

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PyCon 2009: Building Real-time Network applications for the web with Twisted and Orbited (Part 3 of 3)

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PyCon 2009: Building Real-time Network applications for the web with Twisted and Orbited (Part 2 of 3)

Image from PyCon 2009: Building Real-time Network applications for the web with Twisted and Orbited (Part 1 of 3)

PyCon 2009: Building Real-time Network applications for the web with Twisted and Orbited (Part 1 of 3)

Image from PyCon 2009: Data Storage in Python - An Overview of Options (Part 2 of 3)

PyCon 2009: Data Storage in Python - An Overview of Options (Part 2 of 3)

Image from PyCon 2009: Data Storage in Python - An Overview of Options (Part 1 of 3)

PyCon 2009: Data Storage in Python - An Overview of Options (Part 1 of 3)

Image from PyCon 2009: Data Storage in Python - An Overview of Options (Part 3 of 3)

PyCon 2009: Data Storage in Python - An Overview of Options (Part 3 of 3)

Image from PyCon 2009: Django in the Real World (Part 1 of 3)

PyCon 2009: Django in the Real World (Part 1 of 3)

Image from PyCon 2009: Django in the Real World (Part 2 of 3)

PyCon 2009: Django in the Real World (Part 2 of 3)

Image from PyCon 2009: Django in the Real World (Part 3 of 3)

PyCon 2009: Django in the Real World (Part 3 of 3)

Image from PyCon 2009: Django on Jython (#101)

PyCon 2009: Django on Jython (#101)

Image from PyCon 2009: Easy Concurrency with Kamaelia (Part 2 of 2)

PyCon 2009: Easy Concurrency with Kamaelia (Part 2 of 2)

Image from PyCon 2009: Easy Concurrency with Kamaelia (Part 1 of 2)

PyCon 2009: Easy Concurrency with Kamaelia (Part 1 of 2)

Image from PyCon 2009: Eggs and Buildout Deployment in Python (Part 2 of 3)

PyCon 2009: Eggs and Buildout Deployment in Python (Part 2 of 3)

Image from PyCon 2009: Eggs and Buildout Deployment in Python (Part 1 of 3)

PyCon 2009: Eggs and Buildout Deployment in Python (Part 1 of 3)

Image from PyCon 2009: Eggs and Buildout Deployment in Python (Part 3 of 3)

PyCon 2009: Eggs and Buildout Deployment in Python (Part 3 of 3)

Image from PyCon 2009: Faster Python Programs through Optimization (Part 1 of 3)

PyCon 2009: Faster Python Programs through Optimization (Part 1 of 3)

Image from PyCon 2009: Faster Python Programs through Optimization (Part 3 of 3)

PyCon 2009: Faster Python Programs through Optimization (Part 3 of 3)

Image from PyCon 2009: Faster Python Programs through Optimization (Part 2 of 3)

PyCon 2009: Faster Python Programs through Optimization (Part 2 of 3)

Image from PyCon 2009: Functional Testing of Desktop Applications (#1)

PyCon 2009: Functional Testing of Desktop Applications (#1)

Image from PyCon 2009: Giving back and helping expand the Python community. A roadmap for South America and Africa (#87)

PyCon 2009: Giving back and helping expand the Python community. A roadmap for South America and Africa (#87)

Image from PyCon 2009: Hands on with Trac plugins (Part 1 of 2)

PyCon 2009: Hands on with Trac plugins (Part 1 of 2)

Image from PyCon 2009: Hands on with Trac plugins (Part 2 of 2)

PyCon 2009: Hands on with Trac plugins (Part 2 of 2)

Image from PyCon 2009: How AlterWay releases web applications using zc.buildout (#22)

PyCon 2009: How AlterWay releases web applications using zc.buildout (#22)

Image from PyCon 2009: How I Distribute Python applications on Windows - py2exe and InnoSetup (#108)

PyCon 2009: How I Distribute Python applications on Windows - py2exe and InnoSetup (#108)

Image from PyCon 2009: How to Build Applications Linux Distributions will Package (#78)

PyCon 2009: How to Build Applications Linux Distributions will Package (#78)

Image from PyCon 2009: Intermediate Turbogears (Part 1 of 3)

PyCon 2009: Intermediate Turbogears (Part 1 of 3)

Image from PyCon 2009: Intermediate Turbogears (Part 2 of 3)

PyCon 2009: Intermediate Turbogears (Part 2 of 3)

Image from PyCon 2009: Intermediate Turbogears (Part 3 of 3)

PyCon 2009: Intermediate Turbogears (Part 3 of 3)

Image from PyCon 2009: Introduction to Functional Web Testing With Twill and Selenium (Part 2 of 2)

PyCon 2009: Introduction to Functional Web Testing With Twill and Selenium (Part 2 of 2)

Image from PyCon 2009: Introduction to Functional Web Testing With Twill and Selenium (Part 1 of 2)

PyCon 2009: Introduction to Functional Web Testing With Twill and Selenium (Part 1 of 2)

Image from PyCon 2009: Introduction to SQLAlchemy (Part 1 of 3)

PyCon 2009: Introduction to SQLAlchemy (Part 1 of 3)

Image from PyCon 2009: Introduction to SQLAlchemy (Part 2 of 3)

PyCon 2009: Introduction to SQLAlchemy (Part 2 of 3)

Image from PyCon 2009: Introduction to SQLAlchemy (Part 3 of 3)

PyCon 2009: Introduction to SQLAlchemy (Part 3 of 3)

Image from PyCon 2009: Introduction to TurboGears2 and WSGI (Part 2 of 2)

PyCon 2009: Introduction to TurboGears2 and WSGI (Part 2 of 2)

Image from PyCon 2009: Introduction to TurboGears2 and WSGI (Part 1 of 2)

PyCon 2009: Introduction to TurboGears2 and WSGI (Part 1 of 2)

Image from PyCon 2009: Keynote: Reddit: Steve Huffman and Alexis Ohanian

PyCon 2009: Keynote: Reddit: Steve Huffman and Alexis Ohanian

Image from PyCon 2009: Paver: easy build and deployment automation for Python projects (#13)

PyCon 2009: Paver: easy build and deployment automation for Python projects (#13)

Image from PyCon 2009: Plenary: Intro to Sprinting

PyCon 2009: Plenary: Intro to Sprinting

Image from PyCon 2009: Plenary: Sprint Intro

PyCon 2009: Plenary: Sprint Intro

Image from PyCon 2009: py.test II - cross-platform and distributed testing (Part 3 of 3)

PyCon 2009: py.test II - cross-platform and distributed testing (Part 3 of 3)

Image from PyCon 2009: py.test II - cross-platform and distributed testing (Part 1 of 3)

PyCon 2009: py.test II - cross-platform and distributed testing (Part 1 of 3)

Image from PyCon 2009: py.test II - cross-platform and distributed testing (Part 2 of 3)

PyCon 2009: py.test II - cross-platform and distributed testing (Part 2 of 3)

Image from PyCon 2009: Python 102 (Part 1 of 3)

PyCon 2009: Python 102 (Part 1 of 3)

Image from PyCon 2009: Python 102 (Part 2 of 3)

PyCon 2009: Python 102 (Part 2 of 3)

Image from PyCon 2009: Python 102 (Part 3 of 3)

PyCon 2009: Python 102 (Part 3 of 3)

Image from PyCon 2009: Python 401: Some Advanced Topics (Part 1 of 3)

PyCon 2009: Python 401: Some Advanced Topics (Part 1 of 3)

Image from PyCon 2009: Python 401: Some Advanced Topics (Part 2 of 3)

PyCon 2009: Python 401: Some Advanced Topics (Part 2 of 3)

Image from PyCon 2009: Python 401: Some Advanced Topics (Part 3 of 3)

PyCon 2009: Python 401: Some Advanced Topics (Part 3 of 3)

Image from PyCon 2009: Python for Teachers (and Teachers of Teachers) (Part 1 of 2)

PyCon 2009: Python for Teachers (and Teachers of Teachers) (Part 1 of 2)

Image from PyCon 2009: Python for Teachers (and Teachers of Teachers) (Part 2 of 2)

PyCon 2009: Python for Teachers (and Teachers of Teachers) (Part 2 of 2)

Image from PyCon 2009: Scrape the Web: Strategies for programming websites that don't expect it (Part 1 of 3)

PyCon 2009: Scrape the Web: Strategies for programming websites that don't expect it (Part 1 of 3)

Image from PyCon 2009: Scrape the Web: Strategies for programming websites that don't expect it (Part 3 of 3)

PyCon 2009: Scrape the Web: Strategies for programming websites that don't expect it (Part 3 of 3)

Image from PyCon 2009: Scrape the Web: Strategies for programming websites that don't expect it (Part 2 of 3)

PyCon 2009: Scrape the Web: Strategies for programming websites that don't expect it (Part 2 of 3)

Image from PyCon 2009: The (lack of) design patterns in Python (#60)

PyCon 2009: The (lack of) design patterns in Python (#60)

Image from PyCon 2009: The State of Django (#76)

PyCon 2009: The State of Django (#76)

Image from PyCon 2009: The State of the Python Community: Leading the Python tribe (#118)

PyCon 2009: The State of the Python Community: Leading the Python tribe (#118)

Image from PyCon 2009: ToscaWidgets: Test Driven Modular Ajax (Part 2 of 2)

PyCon 2009: ToscaWidgets: Test Driven Modular Ajax (Part 2 of 2)

Image from PyCon 2009: ToscaWidgets: Test Driven Modular Ajax (Part 1 of 2)

PyCon 2009: ToscaWidgets: Test Driven Modular Ajax (Part 1 of 2)

Image from PyCon 2009: Using Twisted Deferreds (Part 1 of 3)

PyCon 2009: Using Twisted Deferreds (Part 1 of 3)

Image from PyCon 2009: Using Twisted Deferreds (Part 2 of 3)

PyCon 2009: Using Twisted Deferreds (Part 2 of 3)

Image from PyCon 2009: Using Twisted Deferreds (Part 3 of 3)

PyCon 2009: Using Twisted Deferreds (Part 3 of 3)

Image from Python 101 (1 of 2)

Python 101 (1 of 2)

Image from Python 101 (2 of 2)

Python 101 (2 of 2)

Image from Tour of Python Standard Library (3 of 3)

Tour of Python Standard Library (3 of 3)

Image from ChiPy: Chicago Python User Group Jan. '08 Meeting

ChiPy: Chicago Python User Group Jan. '08 Meeting