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)

Long short-term memory networks

Long short-term memory networks (LSTMS), are a special type of RNN capable of learning long-term dependencies. While standard RNNs can remember previous states to some extent, they did this on a fairly basic level by updating a hidden state on each time step. This enabled the network to remember short-term dependencies. The hidden state, being a function of previous states, retains information about these previous states. However, the more time steps there are between the current state and a previous state, it diminishes the effect that this earlier state will have on the current state. Far less information is retained on a state that is say 10 time steps before the time step immediately preceding the current step. This is despite that fact that earlier time steps may contain important information with direct relevance to a particular problem or...