PyData Berlin 2014
PyData conferences are a gathering of users and developers of data analysis tools in Python. The goals are to provide Python enthusiasts a place to share ideas and learn from each other about how best to apply the language and tools to ever-evolving challenges in the vast realm of data management, processing, analytics, and visualization.
- July 25, 2014
- Number of videos:
We will give an introduction to the recent development of Deep Neural Networks and focus in particular on Convolution Networks which are well suited to image classification problems. We will also provide you with the practical knowledge of how to get started with using ConvNets via the cuda-convnet python library.
The best filter algorithm to fuse multiple sensor informations is the Kalman filter. To implement it for non-linear dynamic models (e.g. a car), analytic calculations for the matrices are necessary. In this talk, one can see, how the IPython Notebook and Sympy helps to develop an optimal filter to fuse sensor information from different sources (e.g. acceleration, speed and GPS position) to get an optimal estimate. more: http://balzer82.github.io/Kalman/
In this talk I would like to show you a few real-life use-cases where Elasticsearch can help you make sense of your data. We will start with the most basic use case of searching your unstructured data and move on to more advanced topics such as faceting, aggregations and structured search. I would like to demonstrate that the very same tool and dataset can be used for real-time analytics as well as the basis for your more advanced data processing jobs. All in a distributed environment capable of handling terabyte-sized datasets. All examples will be shown with real data and python code demoing the new libraries we have been working on to make this process easier.
This talk is a description of how - against a backdrop of data-drunk tax authorities, legal pressures on businesses to have appropriate compliance systems in place, and the constant pressure on their law firms to commoditise compliance services, Pandas may be about to make a foray from its venerable financial origins into a brave new fiscal world - and can revolutionise an industry by doing so. A case study covering the author's development of a Pandas-based stamp duty land tax engine ("ORVILLE") is discussed, and the inherent usefulness of Pandas in the world of tax analysis is explored.
People talk about a Moore's Law for gene sequencing, a Moore's Law for software, etc. This is talk is about the Moore's Law, the bull that the other "Laws" ride; and how Python-powered ML helps drive it. How do we keep making ever-smaller devices? How do we harness atomic-scale physics? Large-scale machine learning is key. The computation drives new chip designs, and those new chip designs are used for new computations, ad infinitum. High-dimensional regression, classification, active learning, optimization, ranking, clustering, density estimation, scientific visualization, massively parallel processing -- it all comes into play, and Python is powering it all.
Speed without drag: making code faster when there's no time to waste A practical walkthrough over the state-of-the-art of low-friction numerical Python enhancing solutions, covering: exhausting CPython, NumPy, Numba, Parakeet, Cython, Theano, Pyston, PyPy/NumPyPy and Blaze.
This talk shows how to tackle common tasks in applied trend research and technology foresight from identifying a data-source, getting the data and data cleaning to presenting the insights in meaningful visualizations.