If you want to switch your Deep Learning work to PyTorch, this book is for you

By ridhigrg |Email | Jun 20, 2019 | 1395 Views

PyTorch Deep Learning Hands-On: Apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more 

Rapidly build deep learning systems in PyTorch

Key Features
  • Quick start guide to PyTorch internals, principles, and projects
  • Implement key deep learning methods in PyTorch: CNN's, GANs, RNNs, reinforcement learning, and more
  • Build deep learning workflows and take deep learning models from prototyping to production

Book Description
PyTorch is a new, lightweight, and Python-first tool for deep learning. Built by Facebook to offer flexibility and speed, it has quickly become the preferred tool for deep learning experts. PyTorch helps you release deep learning models faster than ever before.

PyTorch Deep Learning Hands-On offers a rapid orientation to PyTorch. Over 8 chapters, it shows how to implement every major deep learning architecture. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language processing (RNN), GANs, and reinforcement learning. It explains how the PyTorch framework supports deep learning workflows, migrates models built in Python to highly efficient TorchScript, and deploys to production using the most sophisticated available tools.

Each chapter focuses on a different area of deep learning. Chapters start with a refresher on the core principles, before sharing the code you need to implement them in PyTorch.

If you want to switch your deep learning work to PyTorch, this book is for you.

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 professionals and enthusiasts who know Python and want to build efficient and powerful deep learning systems in PyTorch. Ideal for anyone looking for a rapid acceleration into PyTorch projects.

Table of Contents
  • Deep Learning Walkthrough and PyTorch Introduction
  • A Simple Neural Network
  • Deep Learning Workflow
  • Computer Vision
  • Sequential Data Processing
  • Generative Networks
  • Reinforcement Learning
  • PyTorch to Production

Source: HOB