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)


It is important to remember that an image detection model does not see an image but a set of pixel color values, or, in the case of a spam filter, a collection of characters in an email. These are raw features of the model. An important part of machine learning is feature transformation. A feature transformation we have already discussed is dimensionality reduction in regard to principle component analysis. The following is a list common feature transformations:

  • Dimensionality reduction to reduce the number of features using techniques such as PCA
  • Scaling or normalizing features to be within a particular numerical range
  • Transforming the feature data type (for example, assigning categories to numbers)
  • Adding random or generated data to augment features

Each feature is encoded on to a dimension of our input tensor, X, so in order to make a learning model as efficient...