PyTorch Pocket Reference: Building and Deploying Deep Learning Models 155367

  From the Preface

We are living in exciting times! Some of us have been fortunate to have lived through huge advances in technology—the invention of the personal computer, the dawn of the internet, the proliferation of cell phones, and the advent of social media. And now, major breakthroughs are happening in AI!

It’s exciting to watch and be a part of this change. I think we’re just getting started, and it’s amazing to think of how the world might change over the next decade. How great it is that we’re living during these times and can participate in the expansion of AI?

PyTorch has, no doubt, enabled some of the finest advances in deep learning and AI. It’s free to download and use, and with it anyone with a computer or internet connection can run AI experiments. In addition to more comprehensive references like this one, there are many free and inexpensive training courses, blog articles, and tutorials that can help you. Anyone can get started using PyTorch for machine learning and AI.

 Who Should Read This Book

This book is written for both beginners and advanced users interested in machine learning and AI. It will help to have some experience writing Python code and a basic understanding of data science and machine learning.

If you’re just getting started in machine learning, this book will help you learn the basics of PyTorch and provide some simple examples. If you’ve been using another framework, such as TensorFlow, Caffe2, or MXNet, the book with help you become familiar with the PyTorch API and its programming mindset so you can expand your skillset.

If you’ve been using PyTorch for a while, this book will help you expand your knowledge on advanced topics like acceleration and optimization and provide a quick-reference resource while you use PyTorch for your day-to-day development.

 Why I Wrote This Book

Learning and mastering PyTorch can be very exciting. There’s so much to explore! When I first started learning PyTorch, I wished I had a single resource that would teach me everything. I wanted something that would give me a good high-level look at what PyTorch had to offer, but also would provide examples and enough details when I needed to dig deeper.

There are some really good books and courses on PyTorch, but they often focus on tensors and training for deep learning models. The PyTorch online documentation is really good, too, and provides a lot of details and examples; however, I found using it was often cumbersome. I kept having to click around to learn or Google what I needed to know. I needed a book on my desk that I could earmark and reference as I was coding.

My goal is that this will be the ultimate PyTorch reference for you. In addition to reading through it to get a high-level understanding of the PyTorch resources available to you, I hope that you earmark the key sections for your development work and keep it on your desk. That way if you forget something, you can get the answer right away. If you prefer ebooks or online books, You can bookmark this book online. However you may use it, I hope the book helps you create some amazing new technology with PyTorch!