Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch

Key Features

Internals and principles of PyTorch

Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more

Build deep learning workflows and take deep learning models from prototyping to production

Book Description

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 them in PyTorch.

This book is ideal if you want to rapidly add PyTorch to your deep learning toolset.

What you will learn

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

Who this book is for

Machine learning engineers who want to put PyTorch to work.