Sherin Thomas - PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
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- Book:PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
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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
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 learnUse 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
Machine learning engineers who want to put PyTorch to work.
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
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First published: April 2019
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Sherin Thomas started his career as an information security expert and shifted his focus to deep learning-based security systems. He has helped several companies across the globe to set up their AI pipelines and worked recently for CoWrks, a fast-growing start-up based out of Bengaluru. Sherin is working on several open source projects including PyTorch, RedisAI, and many more, and is leading the development of TuringNetwork.ai. Currently, he is focusing on building the deep learning infrastructure for [tensor]werk, an Orobix spin-off company.
I am indebted to a multitude of professionals who have influenced me and motivated me to write this book. Among them are my colleagues from CoWrks and my friends. I can't thank enough the technical reviewers and editorial assistants. Without them, I would not have been able to meet the deadlines. Last, and most importantly, I am indebted to my wife, Merin. Writing a book along with a day job is not easy, and without her, it would have been impossible.
Sudhanshu Passi is a technologist employed at CoWrks. Among other things, he has been the driving force behind everything related to machine learning at CoWrks. His expertise in simplifying complex concepts makes his work an ideal read for beginners and experts alike. This can be verified by his many blogs and this debut book publication. In his spare time, he can be found at his local swimming pool computing gradient descent underwater.
I would like to thank Sherin for this opportunity to be a co-author on this book. I would also like to thank my parents for their continuous support throughout the years.
Bharath G. S. is an independent machine learning researcher and he currently works with glib.ai as a machine learning engineer. He also collaborates with mcg.ai as a machine learning consultant. His main research areas of interest include reinforcement learning, natural language processing, and cognitive neuroscience. Currently, he's researching the issue of algorithmic fairness in decision making. He's also involved in the open source development of the privacy-preserving machine learning platform OpenMined as a core collaborator, where he works on private and secure decentralized deep learning algorithms. You can also find some of the machine learning libraries that he has co-authored on PyPI, such as parfit, NALU, and pysyft.
Liao Xingyu is pursuing his master's degree in University of Science and Technology of China ( USTC ). He has ever worked in Megvii.inc and JD AI lab as an intern. He has published a Chinese PyTorch book named Learn Deep Learning with PyTorch in China.
I am grateful for my family's support and project editor Tom's help in producing and reviewing this book.
PyTorch Deep Learning Hands-On is beginner-friendly but also helps readers to get into the depths of deep learning quickly. In the last couple of years, we have seen deep learning become the new electricity. It has fought its way from academia into industry, helping resolve thousands of enigmas that humans could never have imagined solving without it. The mainstream adoption of deep learning as a go-to implementation was driven mainly by a bunch of frameworks that reliably delivered complex algorithms as efficient built-in methods. This book showcases the benefits of PyTorch for prototyping a deep learning model, for building a deep learning workflow, and for taking a prototyped model to production. Overall, the book concentrates on the practical implementation of PyTorch instead of explaining the math behind it, but it also links you to places that you could fall back to if you lag behind with a few concepts.
This book was written with beginners in mind, but won't have them hopping around for another book to move to advanced topics. Hence, we have refrained from explaining the algorithms as much as possible and have instead focused on their implementation in PyTorch, sometimes looking at the implementation of real-world applications using those algorithms. This book is ideal for those who know how to program in Python and understand the basics of deep learning. This book is for people who are practicing traditional machine learning concepts already or who are developers and want to explore the world of deep learning practically and deploy their implementations to production.
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