We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

An introduction to PyTorch & Autograd

Formal Metadata

Title
An introduction to PyTorch & Autograd
Title of Series
Number of Parts
160
Author
License
CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
An introduction to PyTorch & Autograd [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] PyTorch is an optimized tensor library for Deep Learning, and is a recent newcomer to the growing list of GPU programming frameworks available in Python. Like other frameworks it offers efficient tensor representations and is agnostic to the underlying hardware. However, unlike other frameworks it allows you to create ""define-by-run"" neural networks resulting in dynamic computation graphs, where every single iteration can be different---opening up a whole new world of possibilities. Central to all neural networks in PyTorch is the Autograd package, which performs Algorithmic Differentiation on the defined model and generates the required gradients at each iteration. In this talk I will present a gentle introduction to the PyTorch library and overview its main features using some simple examples, paying particular attention to the mechanics of the Autograd package. Keywords: GPU Processing, Algorithmic Differentiation, Deep Learning, Linear algebra