Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Understanding Hugging Face within the PyTorch context

Hugging Face [1] is a rapidly growing multi-faceted AI company. On the one hand, it provides a host of libraries related to training, evaluating, optimizing, and deploying AI models. On the other hand, it is a hub of various AI models, datasets, and live AI demos (referred to as spaces in Hugging Face jargon). Hugging Face is quickly evolving into an AI community where developers are sharing cutting-edge AI work and having discussions that push the frontier of AI.

Exploring Hugging Face components relevant to PyTorch

We can view Hugging Face as a platform that contains various components, as shown in Figure 19.1. You can access the page shown in the figure on the Hugging Face website [2]. The first thing to note in this figure is the mention of PyTorch in various Hugging Face components. Hugging Face’s libraries, models, and datasets are fully compatible with PyTorch, so it is wise to discuss Hugging Face in detail...