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Classroom Contents
PyTorch - Fast Differentiable Dynamic Graphs in Python
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- 1 Intro
- 2 Overview of the talk
- 3 Machine Translation
- 4 Adversarial Networks
- 5 Adversarial Nets
- 6 Chained Together
- 7 Trained with Gradient Descent
- 8 Computation Graph Toolkits Declarative Toolkits
- 9 Imperative Toolkits
- 10 Seamless GPU Tensors
- 11 Neural Networks
- 12 Python is slow
- 13 Types of typical operators
- 14 Add - Mul A simple use-case
- 15 High-end GPUs have faster memory
- 16 GPUs like parallelizable problems
- 17 Compilation benefits
- 18 Tracing JIT
