This book is for anyone who wants a straightforward, practical introduction to deep learning using PyTorch. The aim is to give you an understanding of deep learning models by direct experimentation. This book is perfect for those who are familiar with Python, know some machine learning basics, and are looking for a way to productively develop their skills. The book will focus on the most important features and give practical examples. It assumes you have a working knowledge of Python and are familiar with the relevant mathematical ideas, including with linear algebra and differential calculus. The book provides enough theory to get you up and running without requiring rigorous mathematical understanding. By the end of the book, you will have a practical knowledge of deep learning systems and able to apply PyTorch models to solve the problems that you care about.

#### Deep Learning with PyTorch Quick Start Guide

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#### Deep Learning with PyTorch Quick Start Guide

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#### 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)

Preface

Free Chapter

Introduction to PyTorch

Deep Learning Fundamentals

Computational Graphs and Linear Models

Convolutional Networks

Other NN Architectures

Getting the Most out of PyTorch

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