Deep Learning with PyTorch Quick Start Guide

More Information
Learn
  • Set up the deep learning environment using the PyTorch library
  • Learn to build a deep learning model for image classification
  • Use a convolutional neural network for transfer learning
  • Understand to use PyTorch for natural language processing
  • Use a recurrent neural network to classify text
  • Understand how to optimize PyTorch in multiprocessor and distributed environments
  • Train, optimize, and deploy your neural networks for maximum accuracy and performance
  • Learn to deploy production-ready models
About

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.

This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders.

You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.

By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.

Features

Clear and concise explanations Gives important insights into deep learning models Practical demonstration of key concepts

Page Count 118
Course Length 3 hours 32 minutes
ISBN 9781789534092
Date Of Publication 24 Dec 2018

Authors

David Julian

David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was a technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.