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)

Convolutional Networks

Previously, we built several simple networks to solve regression and classification problems. These illustrated the basic code structure and concepts involved in building ANNs with PyTorch.

In this chapter, we will extend simple linear models by adding layers and using convolutional layers to solve nonlinear problems found in real-world examples. Specifically, we will cover the following topics:

  • Hyper-parameters and multilayered networks
  • Build a simple benchmarking function to train and test models
  • Convolutional networks