Book Image

Deep Learning with PyTorch Quick Start Guide

By : David Julian
Book Image

Deep Learning with PyTorch Quick Start Guide

By: David Julian

Overview of this book

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text. By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.
Table of Contents (8 chapters)


We have covered a lot of material in this chapter. Don't worry if you do not understand some of the mathematics presented here. The aim is to give you some intuition into how some common machine learning algorithms work, not to have a complete understanding of the theory behind these algorithms. After reading this chapter, you should have some understanding of the following:

  • General approaches to machine learning, including knowing the difference between supervised and unsupervised methods, online and batch learning, and rule-based, as opposed to model-based, learning
  • Some unsupervised methods and their applications, such as clustering and principle component analysis
  • Types of classification problems, such as binary, multi-class, and multi-out classification
  • Features and feature transformations
  • The mechanics of linear regression and gradient descent
  • An overview of...