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Make sense of Deep Neural Networks using TensorBoard

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In this talk we look at some ways in which the TensorBoard utility can be used to better understand the structure of Deep Neural Networks and how they function. Best practices on how to use the TensorFlow Python API to make your models and results more interpretable are discussed. ​ Abstract Deep Neural Networks are fast becoming the face of modern Machine Learning. But understanding how they work can be a real challenge, especially while you are trying to build a model. Google's recently published library, TensorFlow, includes a lesser-used utility called TensorBoard that can be used to visualize the structure of your neural network model and inspect how data flows through it. This talk will demonstrate some techniques which will help you use TensorBoard more effectively, and better understand how TensorFlow computations work. Code walkthroughs will be done in iPython notebooks, which will be made available to attendees.

Bio: Arpan likes to find computing solutions to everyday problems. He is interested in human-computer interaction, robotics and cognitive science. He obtained his PhD from North Carolina State University, focusing on biologically-inspired computer vision. ​Working at Udacity, he develops content for artificial intelligence and machine learning courses.

https://speakerdeck.com/pybay2016/arpan-chakraborty-make-sense-of-deep-neural-networks-using-tensorboard

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