Anirudh Koul - Practical deep learning for cloud, mobile, and edge: real-world AI and computer-vision projects using Python, Keras, and TensorFlow

by Anirudh Koul , Siddha Ganju , and Meher Kasam

Copyright 2020 Anirudh Koul, Siddha Ganju, Meher Kasam. All rights reserved.

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978-1-492-03486-5

[LSI]

We are experiencing a renaissance of artificial intelligence, and everyone and their neighbor wants to be a part of this movement. Thats quite likely why you are browsing through this book. There are tons of books about deep learning out there. So you might ask us, very reasonably, why does this book even exist? Well get to that in just a second.

During our own deep learning journeys since 2013 (while building products at companies including Microsoft, NVIDIA, Amazon, and Square), we witnessed dramatic shifts in this landscape. Constantly evolving research was a given and a lack of mature tooling was a reality of life.

While growing and learning from the community, we noticed a lack of clear guidance on how to convert research to an end product for everyday users. After all, the end user is somewhere in front of a web browser, a smartphone, or an edge device. This often involved countless hours of hacking and experimentation, extensively searching through blogs, GitHub issue threads, and Stack Overflow answers, and emailing authors of packages to get esoteric knowledge, as well as occasional Aha! moments. Even the books on the market tended to focus more on theory or how to use a specific tool. The best we could hope to learn from the available books was to build a toy example.

To fill this gap between theory and practice, we originally started giving talks on taking artificial intelligence from research to the end user with a particular focus on practical applications. The talks were structured to showcase motivating examples, as well as different levels of complexity based on skill level (from a hobbyist to a Google-scale engineer) and effort involved in deploying deep learning in production. We discovered that beginners and experts alike found value in these talks.

Over time, the landscape thankfully became accessible to beginners and more tooling became available. Great online material like Fast.ai and DeepLearning.ai made understanding how to train AI models easier than ever. Books also cornered the market on teaching fundamentals using deep learning frameworks such as TensorFlow and PyTorch. But even with all of this, the wide chasm between theory and production remained largely unaddressed. And we wanted to bridge this gap. Thus, the book you are now reading.

Using approachable language as well as ready-to-run fun projects in computer vision, the book starts off with simple classifiers assuming no knowledge of machine learning and AI, gradually building to add complexity, improve accuracy and speed, scale to millions of users, deploy on a wide variety of hardware and software, and eventually culminate in using reinforcement learning to build a miniature self-driving car.

Nearly every chapter begins with a motivating example, establishes the questions upfront that one might ask through the process of building a solution, and discusses multiple approaches for solving problems, each with varying levels of complexity and effort involved. If you are seeking a quick solution, you might end up just reading a few pages of a chapter and be done. Someone wanting to gain a deeper understanding of the subject should read the entire chapter. Of course, everyone should peruse the case studies included in these chapters for two reasonsthey are fun to read and they showcase how people in the industry are using the concepts discussed in the chapter to build real products.

We also discuss many of the practical concerns faced by deep learning practitioners and industry professionals in building real-world applications using the cloud, browsers, mobile, and edge devices. We compiled a number of practical tips and tricks, as well as life lessons in this book to encourage our readers to build applications that can make someones day just a little bit better.