PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with PyTorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.
PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools.
Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement it in PyTorch.
This book is ideal if you want to rapidly add PyTorch to your deep learning toolset.
Use PyTorch to build:
Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and more
Convolutional Neural Networks – create advanced computer vision systems
Recurrent Neural Networks – work with sequential data such as natural language and audio
Generative Adversarial Networks – create new content with models including SimpleGAN and CycleGAN
Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing
Deep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages
Production-ready models – package your models for high-performance production environments