PyTorch - A Modern Library for Machine Learning with Adam Paszke

Explore the world of PyTorch, a modern library for machine learning, in this comprehensive talk by Adam Paszke, co-author and maintainer of PyTorch. Delve into the underlying ideas of the library and discover how it can be applied across various machine learning scenarios, from research to production. Learn about PyTorch's roots in research applications and its recent focus on efficient inference functionality. Gain insights into multidimensional arrays, accelerator support, automatic differentiation, polymorphic code, and stochastic gradient descent. Understand the benefits of using PyTorch, including its hierarchical structure, implementation details, and performance advantages. Discover how PyTorch addresses the needs of both researchers and industry professionals, offering solutions for mobile apps, archives, and packages. Join this informative session to enhance your understanding of this essential tool in the machine learning ecosystem.

Introduction
What is PyTorch
What is a multidimensional array
Accelerator support
Automatic differentiation
Polymorphic code
Stochastic gradient descent
Hierarchical structure
Implementation
Model Study
Why use PyTorch
Benefits of PyTorch
The real question
Statically typed language
Inheritance
Exporting
Mobile Apps
Archives
Packages
Performance
Thank you
Questions Answers