Preface
Deep learning is driving the AI revolution and PyTorch is making it easier than ever before for anyone to build deep learning applications. This book will help you uncover expert techniques and gain insights 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. Similarly, you will explore recurrent neural network (RNN) architectures as well as Transformers and use them for sentiment analysis. Next, you will learn how to create arbitrary neural network architectures and build Graph neural networks (GNNs). As you advance, you’ll apply deep learning (DL) across different domains such as music, text, and image generation using generative models including Generative adversarial networks (GANs) and diffusion.
Next, you’ll build and train your own deep reinforcement learning models in PyTorch, as well as interpreting DL models. You will not only learn how to build models but also how to deploy them into production and to mobile devices (Android and iOS) using expert tips and techniques. Next, you will master the skills of training large models efficiently in a distributed fashion, searching neural architectures effectively with AutoML, as well as rapidly prototyping models using fastai. You’ll then create a recommendation system using PyTorch. Finally, you’ll use major Hugging Face libraries together with PyTorch to build cutting edge artificial intelligence (AI) models.
By the end of this PyTorch book, you’ll be well equipped to perform complex deep learning tasks using PyTorch to build smart AI models.
