Preface

Deep learning (DL) is driving the AI revolution, and PyTorch is making it easier than ever for people to build DL applications. This book will help you discover expert techniques to get the most out of your data and build complex neural network models.

The book starts with a quick overview of PyTorch and explores convolutional neural network (CNN) architectures for image classification. You will explore recurrent neural network (RNN) architectures as well as Transformers and use them for sentiment analysis. As you advance, you'll apply DL across different domains, such as music, text, and image generation, using generative models. After that, you'll delve into the world of generative adversarial networks (GANs), build and train your own deep reinforcement learning models in PyTorch, and interpret DL models. You will not only learn how to build models but also deploy PyTorch models into production using expert tips and techniques. Finally, you will master the skill of training large models efficiently in a distributed fashion, search neural architectures effectively with AutoML, and rapidly prototype models using PyTorch and fast.ai.

By the end of this PyTorch book, you'll be well equipped to perform complex DL tasks using PyTorch to build smart artificial intelligence models.