Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python,

** Featured as a learning resource on the official Keras website **

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

Exploring the Landscape of Artificial Intelligence

What's in the Picture: Image Classification with Keras

Cats Versus Dogs: Transfer Learning in 30 Lines with Keras

Building a Reverse Image Search Engine: Understanding Embeddings

From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy

Maximizing Speed and Performance of TensorFlow: A Handy Checklist

Practical Tools, Tips, and Tricks

Cloud APIs for Computer Vision: Up and Running in 15 Minutes

Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow

AI in the Browser with TensorFlow.js and ml5.js

Real-Time Object Classification on iOS with Core ML

Not Hotdog on iOS with Core ML and Create ML

Shazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit

Building the Purrfect Cat Locator App with TensorFlow Object Detection API

Becoming a Maker: Exploring Embedded AI at the Edge

Simulating a Self-Driving Car Using End-to-End Deep Learning with Keras

Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer

Guest-contributed Content

The 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

).

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