PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
Language: English
Published by Packt Publishing, 2019
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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In English.
Seller Inventory # ria9781788834131_new
- Title
- PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
- Author
- Sherin Thomas
- Publisher
- Packt Publishing
- Publication year
- 2019
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1788834135
- ISBN 13
- 9781788834131
- Item weight
- 557 grams
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
Book Description
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 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 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
"Synopsis" may belong to another edition of this title.
About the Author
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.
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.
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 parft, NALU, and pysyft.
"About the title" may belong to another edition of this title.
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