Mastering PyTorch, Second Edition
- Paperback: 538 pages
- Publisher: WOW! eBook; 2nd edition (July 11, 2023)
- Language: English
- ISBN-10: 1801074305
- ISBN-13: 978-1801074308
Mastering PyTorch, 2nd Edition: 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 Mastering PyTorch, Second Edition 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.
- Implement text, image, and music generating models using PyTorch
- Build a deep Q-network (DQN) model in PyTorch
- Deploy PyTorch models on mobiles and embedded devices
- Become well-versed with rapid prototyping using PyTorch with fast.ai
- Perform neural architecture search effectively using AutoML
- Easily interpret machine learning models using Captum
- Develop your own recommendation system using TorchRec
- Design ResNets, LSTMs, and graph neural networks
- Create language and vision transformer models using Hugging Face
By the end of this Mastering PyTorch, 2nd Edition book, you’ll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.