Up and Running with PyTorch by Pearson

Course details

Are you looking to learn more about PyTorch, the popular, open-source deep learning framework? In this course, get an introduction to PyTorch, and explore the power of its deep learning ecosystem. Learn how the combination of automatic differentiation and transparent computation on GPUs have led to a proliferation of deep learning research. Along the way, get tips on how to use PyTorch to implement and maintain a linear regression model as a stepping stone to more complex neural networks. By the end of this course, you’ll be equipped with in-demand skills to leverage built-in PyTorch components to create a simple feedforward multilayer perceptron.

Concepts

Introduction

Get up and running with PyTorch

PyTorch for the Impatient

What is PyTorch
The PyTorch layer cake
The deep learning software trilemma
What are tensors, really
Tensors in PyTorch
Introduction to computational graphs
Backpropagation is just the chain rule
Effortless backpropagation with torch.autograd
PyTorch's device abstraction - GPUs
Working with devices
Components of a learning algorithm
Introduction to gradient descent
Getting to stochastic gradient descent (SGD)
Comparing gradient descent and SGD
Linear regression with PyTorch
Perceptrons and neurons
Layers and activations with torch.nn
Multilayer feedforward neural networks (MLP)

Conclusion

Course summary