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Jibin Mathew - PyTorch Artificial Intelligence Fundamentals

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Artificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, youll get to grips with building deep learning apps, and how you can use PyTorch for research and solving real-world problems.This book uses a recipe-based approach, starting with the basics of tensor manipulation, before covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in PyTorch. Once you are well-versed with these basic networks, youll build a medical image classifier using deep learning. Next, youll use TensorBoard for visualizations. Youll also delve into Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) before finally deploying your models to production at scale. Youll discover solutions to common problems faced in machine learning, deep learning, and reinforcement learning. Youll learn to implement AI tasks and tackle real-world problems in computer vision, natural language processing (NLP), and other real-world domains.By the end of this book, youll have the foundations of the most important and widely used techniques in AI using the PyTorch framework.

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PyTorch Artificial Intelligence Fundamentals A recipe-based approach to - photo 1
PyTorch Artificial Intelligence Fundamentals
A recipe-based approach to design, build and deploy your own AI models with PyTorch 1.x
Jibin Mathew

BIRMINGHAM - MUMBAI PyTorch Artificial Intelligence Fundamentals Copyright - photo 2

BIRMINGHAM - MUMBAI
PyTorch Artificial Intelligence Fundamentals

Copyright 2020 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Amey Varangoankar
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First published: February 2020

Production reference: 1270220

Published by Packt Publishing Ltd.
Livery Place
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Birmingham
B3 2PB, UK.

ISBN 978-1-83855-704-1

www.packt.com

"I can do all things through Christ who strengthens me."
- Philippians 4:13
Contributors
About the author

Jibin Mathew is a senior data scientist and machine learning researcher who has worked in the AI domain for more than 7 years. He is a serial entrepreneur and has founded multiple AI start-ups. He has a strong software engineering background and understands the complete workflow, from research to scalable production deployment. He has built solutions in the fields of healthcare, environment, finance, industrial monitoring, and retail. He has been an adviser to various companies in their AI endeavors. He was the winner of Singularity University's Global Impact Challenge 2018 and has been part of various global platforms. He is an active contributor to the community and shares his knowledge by authoring content and through blog posts.

About the reviewers

Ganesh Naik is an author, consultant and corporate trainer in the artificial intelligence, data science, machine learning, and embedded Linux product development fields. He is a graduate in computer engineering and has industry experience. He has authored books such as Mastering Python Scripting for System Administrators and Learning Linux Shell Scripting and is the coauthor of Bash Cookbook, Packt Publishing. Ganesh has a passion for teaching. He has trained thousands of engineers in AI, ML, and Linux product development. He has worked as a corporate trainer for corporates such as the Indian Space Research Organisation, Intel, GE, Samsung, Motorola, and various other corporates in India and various other countries.

Rafal Pronko has been a data scientist for 6 years. In his professional life, he has worked on projects such as ad categorization, extracting information from short descriptions; object detection (Smart Shelf and CleanAI projects); and recognition, face, and anti-spoofing recognition mechanisms (the SmartBar anti-spoofing app). Now, he is working for CV Timeline on extracting information from unstructured text such as resumes and social media profiles, categorizing the data, and helping recruiters make better decisions.

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Preface

ArtificialIntelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, you'll get to grips with building deep learning apps, and how you can use PyTorch for research and solving real-world problems.
This book uses a recipe-based approach, starting with the basics of tensor manipulation, before covering ConvolutionalNeural Networks (CNNs) and Recurrent Neural Networks (RNNs) in PyTorch. Once you are well-versed with these basic networks, you'll build a medical image classifier using deep learning. Next, you'll use TensorBoard for visualizations. You'll also delve into GenerativeAdversarialNetworks (GANs) and DeepReinforcementLearning (DRL) before finally deploying your models to production at scale. You'll discover solutions to common problems faced in machine learning, deep learning, and reinforcement learning. You'll learn to implement AI tasks and tackle real-world problems in computer vision, naturallanguageprocessing (NLP

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