Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

by Ashish Ranjan Jha

Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most from your data and build complex neural network models. You’ll create convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) 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. You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production, including mobiles and embedded 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 fast.ai for prototyping models to training models using PyTorch Lightning. You’ll discover libraries for AutoML and explainable AI, create recommendation systems using TorchRec, and build language and vision transformers with Hugging Face. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is an ideal resource for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python programming is required.

558 pages, Paperback

Published May 31, 2024

Reviews

[Travis Jones · Rating 3 out of 5]

In my opinion the title of this book is misleading. It is very basic, and the examples are outdated and repetitious throughout. You are better off reading the PyTorch documentation raw. (If you do that, skim through the first part "torch" that goes over operations and get to "torch.nn" quickly. Take your time there.) Use your time wisely, I feel I did not with these 550 pages.