Book Image

Deep Learning with PyTorch Quick Start Guide

By : David Julian
Book Image

Deep Learning with PyTorch Quick Start Guide

By: David Julian

Overview of this book

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.

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Table of Contents (8 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to PyTorch
Introduction to PyTorch
What is PyTorch?
Installing PyTorch
Basic PyTorch operations
Loading data
Summary
2
Deep Learning Fundamentals
Deep Learning Fundamentals
Approaches to machine learning
Learning tasks
Features
Models
Artificial neural networks
Summary
3
Computational Graphs and Linear Models
Computational Graphs and Linear Models
autograd
Linear models
Multi-class classification example
Summary
4
Convolutional Networks
Convolutional Networks
Hyper-parameters and multilayered networks
Benchmarking models
Convolutional networks
Summary
5
Other NN Architectures
Other NN Architectures
Introduction to recurrent networks
Long short-term memory networks
Summary
6
Getting the Most out of PyTorch
Getting the Most out of PyTorch
Multiprocessor and distributed environments
Optimization techniques
Pretrained models
Summary
7
Other Books You May Enjoy
Other Books You May Enjoy
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Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Deep Learning with PyTorch
Vishnu Subramanian

ISBN: 978-1-78862-433-6

  • Use PyTorch for GPU-accelerated tensor computations
  • Build custom datasets and data loaders for images and test the models using torchvision and torchtext
  • Build an image classifier by implementing CNN architectures using PyTorch
  • Build systems that do text classification and language modeling using RNN, LSTM, and GRU
  • Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning
  • Learn how to mix multiple models for a powerful ensemble model
  • Generate new images using GANs and generate artistic images using style transfer

TensorFlow Machine Learning Cookbook - Second Edition
Nick McClure

ISBN: 978-1-78913-168-0

  • Become familiar with the basic features...
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