Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.

Relying on decades of combined industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.

• Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.
• Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.
• Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies.
• Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.
• Use transfer learning to train models in minutes.
• Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.

List of Chapters

1. Exploring the Landscape of Artificial Intelligence
2. What's in the Picture: Image Classification with Keras
3. Cats Versus Dogs: Transfer Learning in 30 Lines with Keras
4. Building a Reverse Image Search Engine: Understanding Embeddings
5. From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy
6. Maximizing Speed and Performance of TensorFlow: A Handy Checklist
7. Practical Tools, Tips, and Tricks
8. Cloud APIs for Computer Vision: Up and Running in 15 Minutes
9. Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow
10. AI in the Browser with TensorFlow.js and ml5.js
11. Real-Time Object Classification on iOS with Core ML
12. Not Hotdog on iOS with Core ML and Create ML
13. Shazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit
14. Building the Purrfect Cat Locator App with TensorFlow Object Detection API
15. Becoming a Maker: Exploring Embedded AI at the Edge
16. Simulating a Self-Driving Car Using End-to-End Deep Learning with Keras
17. Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer

Guest-contributed ContentThe book features chapters from the following industry experts:

• Sunil Mallya (Amazon AWS DeepRacer)
• Aditya Sharma and Mitchell Spryn (Microsoft Autonomous Driving Cookbook)
• Sam Sterckval (Edgise)
• Zaid Alyafeai (TensorFlow.js)
• 
  

The book also features content contributed by several industry veterans including François Chollet (Keras, Google), Jeremy Howard (Fast.ai), Pete Warden (TensorFlow Mobile), Anima Anandkumar (NVIDIA), Chris Anderson (3D Robotics), Shanqing Cai (TensorFlow.js), Daniel Smilkov (TensorFlow.js), Cristobal Valenzuela (ml5.js), Daniel Shiffman (ml5.js), Hart Woolery (CV 2020), Dan Abdinoor (Fritz), Chitoku Yato (NVIDIA Jetson Nano), John Welsh (NVIDIA Jetson Nano), and Danny Atsmon (Cognata).

Review

"Practical leads the title for good reason. For today's ML practices in industry, two priorities loom: staff needs upskilling and models need fine-tuning. This book fast-tracks both." -- Paco Nathan, Founder, Derwen AI,formerly Director at O'Reilly Media.

From the Author

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 in complexity, improving accuracy and speed, scaling to millions of users, deploying on a wide variety of hardware and software, eventually culminating 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 to solve the problem, 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 at the end of each chapter for two reasons--they are fun to read and they showcase how people in the industry are using the concepts discussed in the chapter to build real products (over 40 discussed).

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 someone's day just a little bit better.

To the Backend/Frontend/Mobile Software DeveloperYou are quite likely a proficient programmer already. Even if Python is an unfamiliar language to you, we expect that you will be able to pick it up easily and get started in no time. Best of all, we don't expect you to have any background in machine learning and AI; that's what we are here for! We believe that you will gain value from the book's focus in the following areas:

• How to build user-facing AI products.
• How to train models quickly.
• How to minimize the code and effort required in prototyping.
• How to make models more performant and energy-efficient.
• How to operationalize and scale, and estimate the costs involved.
• Discover how AI is applied in the industry with 40+ case studies.
• Develop a broad-spectrum knowledge of deep learning.
• Develop a generalized skill set that can be applied on new frameworks (e.g., PyTorch), domains (e.g., healthcare, robotics), input modalities (e.g., video, audio, text), and tasks (e.g., image segmentation, one-shot learning).

To the Data ScientistYou might already be proficient at machine learning and potentially know how to train deep learning models. Good news! You can further enrich your skillset and deepen your knowledge in the field in order to then build real products. This book will help inform your everyday work and beyond by covering how to:

• Speed up your training, including on multi-node clusters.
• Build an intuition for developing and debugging models, including hyperparameter tuning, thus dramatically improving model accuracy.
• Understand how your model works, uncover bias in the data, and automatically determine the best hyperparameters as well as model architecture using AutoML.
• Learn tips and tricks used by other data scientists, including gathering data quickly, tracking your experiments in an organized manner, sharing your models with the world, and being up to date on the best available models for your task.
• Use tools to deploy and scale your best model to real users, and even automatically (without involving a dev-ops team).

To the StudentThis is a great time to be considering a career in AI--this is turning out to be the next revolution in technology after the internet and smartphones. A lot of strides have been made, and a lot remains to be discovered. We hope that this book can serve as your first step in whetting your appetite for a career in AI and, even better, developing deeper theoretical knowledge. And the best part is that you don't have to spend a lot of money to buy expensive hardware. In fact, you can train on powerful hardware entirely for free from your web browser (thank you, Google Colab!). With this book, we hope you will:

• Aspire to a career in AI by developing a portfolio of interesting projects.
• Learn from industry practices to help prepare for internships and job opportunities.
• Unleash your creativity by building fun applications like an autonomous car.
• Prepare for interviews for jobs in the AI field.
• Become an AI for Good champion by using your creativity to solve the most pressing problems faced by humanity.

To the TeacherWe believe that this book can nicely supplement your coursework with fun, real-world projects. We've covered every step of the deep learning pipeline in detail, along with techniques on how to execute each step effectively and efficiently. Each of the projects we present in the book can make for great collaborative or individual work in the classroom throughout the semester.

To the Robotics EnthusiastRobotics is exciting. If you're a robotics enthusiast, we don't really need to convince you that adding intelligence to robots is the way to go. Increasingly capable hardware platforms such as Raspberry Pi, NVIDIA Jetson Nano, Google Coral, Intel Movidius, PYNQ-Z2, and others are helping drive innovation in the robotics space. As we grow towards Industry 4.0, (some of) these platforms will become more and more relevant and ubiquitous. With this book, you will:

• Learn how to build and train AI, and then bring it to the edge.
• Benchmarking and compare edge devices on performance, size, power, battery and costs.
• Understand how to choose the optimal AI algorithm and device for a given scenario.
• Learn on how other makers are building creative robots and machines.
• Learn how to further progress in the field and showcase your work.

About the Author

Anirudh Koul is a noted AI expert, NASA ML Lead, UN/TEDx speaker and a former scientist at Microsoft AI & Research, where he founded Seeing AI, the most used technology among the blind community, after the iPhone. With features shipped to a billion users, he brings over a decade of production-oriented applied research experience on petabyte-scale datasets. His work in the AI for Good field, which IEEE has called 'life-changing', has received awards from CES, FCC, MIT, Cannes Lions, American Council of the Blind, showcased at events by UN, World Economic Forum, White House, House of Lords, Netflix, National Geographic, and lauded by world leaders including Justin Trudeau and Theresa May.

Siddha Ganju, an AI researcher who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. As an AI Advisor to NASA FDL, she helped build an automated meteor detection pipeline for the CAMS project at NASA, which ended up discovering a comet. Previously at Deep Vision, she developed deep learning models for edge devices. Her work ranges from Visual Question Answering to GANs to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. She has served as a featured jury member in several international tech competitions including CES.

Meher Kasam is a seasoned software developer with apps used by tens of millions of users every day. Currently an iOS developer at Square, and having previously worked at Microsoft and Amazon, he has shipped features for a range of apps from Square's Point of Sale to the Bing iPhone app. At Microsoft, he was the mobile dev lead for the Seeing AI app, which has received many awards from Mobile World Congress, CES, FCC, and the American Council of the Blind, to name a few. A hacker at heart, he won several hackathons and shipped features in widely used products. He serves as a judge of international competitions including Global Mobile Awards and Edison Awards.