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Differentiable Graphics with TensorFlow 2.0

Abstract

Deep learning has introduced a profound paradigm change in the recent years, allowing to solve significantly more complex perception problems than previously possible. This paradigm shift has positively impacted a tremendous number of fields with a giant leap forward in computer vision and computer graphics algorithms. The development of public libraries such as Tensorflow are in a large part responsible for the massive growth of AI. These libraries made deep learning easily accessible to every researchers and engineers allowing fast advances in developing deep learning techniques in the industry and academia. We will start this course with an introduction to deep learning and present the newly released TensorFlow 2.0 with a focus on best practices and new exciting functionalities. We will then show different tips, tools, and algorithms to visualize and interpret complex neural networks by using TensorFlow. Finally, we will introduce a novel TensorFlow library containing a set of graphics inspired differentiable layers allowing to build structured neural networks to solve various two and three dimensional perception tasks. To make the course interactive we will punctuate the presentations with real time demos in the form of Colab notebooks. Basic prior familiarity with deep learning will be assumed.

Materials

[PDF]

Speakers

Shan Carter @shancarter Josh Gordon @random_forests
Christian Häne Julien Valentin @JPCValentin

Moderator

Sofien Bouaziz @_sofien_

Organizers

Julien Valentin @JPCValentin Sofien Bouaziz @_sofien_

Special thanks

Paige Bailey @DynamicWebPaige Alexander Mordvintsev @zzznah Martin Wicke @martin_wicke

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