PyTorch - Fast Differentiable Dynamic Graphs in Python
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Overview
Syllabus
Intro
Overview of the talk
Machine Translation
Adversarial Networks
Adversarial Nets
Chained Together
Trained with Gradient Descent
Computation Graph Toolkits Declarative Toolkits
Imperative Toolkits
Seamless GPU Tensors
Neural Networks
Python is slow
Types of typical operators
Add - Mul A simple use-case
High-end GPUs have faster memory
GPUs like parallelizable problems
Compilation benefits
Tracing JIT
Taught by
Strange Loop Conference