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)

What this book covers

Chapter 1, Introduction to PyTorch, gets you up and running with PyTorch, demonstrates its installation on a variety of platforms, and explores key syntax elements and how to import and use data in PyTorch.

Chapter 2, Deep Learning Fundamentals, is a whirlwind tour of the basics of deep learning, covering the mathematics and theory of optimization, linear networks, and neural networks.

Chapter 3, Computational Graphs and Linear Models, demonstrates how to calculate the error gradient of a linear network and how to harness it to classify images.

Chapter 4, Convolutional Networks, examines the theory of convolutional networks and how to use them for image classification.

Chapter 5, Other NN Architectures, discusses the theory behind recurrent networks and shows how to use them to make predictions about sequence data. It also discusses long short-term memory networks (LSTMs) and has you build a language model to predict text.

Chapter 6, Getting the Most out of PyTorch, examines some advanced features, such as using PyTorch in multiprocessor and parallel environments. You will build a flexible solution for image classification using out-of-the-box pre-trained models.