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 2020 Packt Publishing
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First published: February 2020
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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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