Deep Learning using PyTorch

Deep Learning using PyTorch

Presented By Soumith Chintala
Soumith Chintala
Soumith Chintala
Researcher at Facebook AI

Soumith Chintala is a Researcher at Facebook AI Research. He works on deep learning and high performance computing

Presentation Description

Overview

This talk will cover PyTorch, a new deep learning framework that enables new-age A.I. research using dynamic computation graphs. It looks at the architecture of PyTorch and discusses some of the reasons for key decisions in designing it and subsequently look at the resulting improvements in user experience and performance.


Learning Outcomes:

  • You will learn about deep learning using the PyTorch framework
  • You will learn about the similarities and differences between PyTorch, Numpy, and TensorFlow
  • You will learn about the current and future state of Artificial Intelligence
  • You will know about several tools for AI development


Talk Timeline


0:00 - 2:25: Introduction and Overview


2:25 - 10:15:  Ndarray Library

  • Pytorch is a Ndarry Library
  • Example of Numpy vs PyTorch
  • Numpy and PyTorch have a lot of similarities
  • PyTorch has a library library
  • Numpy bridge
  • Seamless GPU Tensors
  • Benefits of PyTorch is that GPUs are fast.


10:15 - 19:18:  Automatic Differentiation Engine

  • Deep learning frameworks provide gradient computation
  • Provide integration with high-performance DL libraries like CuDNN
  • PyTorch uses Autograd for tape-based auto differentiation
  • Tensorflow vs PyTorch side by side comparison
  • Key difference between TF and PyT is that you don’t have to construct graph ahead of time


19:18 - 30:54: Motivation Behind Design of Torch.autrograd

  • Dense captioning system and DeepMask examples
  • Static datasets + Static model structure/Offline Learning
  • Implementation of Neural Networks in Video Games
  • Self-adding new memory or layers changing evaluation path based on inputs
  • There’s a huge need for a dynamic auto-diff


30:54 - 37:04:  Questions

  • How computationally expensive is it to change the graph dynamically?
  • What is driving the changes in the graph are you optimizing the loss?
  • Are you able to optimize the computation of the graph in any way?
  • Is there plans to have higher level libraries that use PyTorch as a backend?
  • Does PyTorch support quantization to 8-bit?

Presentation Curriculum

Deep Learning using PyTorch
37:04
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