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)

Deep Learning Fundamentals

Deep learning is generally considered a subset of machine learning, involving the training of artificial neural networks (ANNs). ANNs are at the forefront of machine learning. They have the ability to solve complex problems involving massive amounts of data. Many of the principles of machine learning generally are also important in deep learning specifically, so we will spend some time reviewing these here.

In this chapter, we will discuss the following topics:

  • Approaches to machine learning
  • Learning tasks
  • Features
  • Models
  • Artificial neural networks