<div>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.<br><br>Relying on years of 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.<br><ul><li>Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.</li><li>Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.</li><li>Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies.</li><li>Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.</li><li>Use transfer learning to train models in minutes.</li><li>Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.</li></ul><div><b>List of Chapters</b></div><div><ol><li>Exploring the Landscape of Artificial Intelligence</li><li>What's in the Picture: Image Classification with Keras</li><li>Cats Versus Dogs: Transfer Learning in 30 Lines with Keras</li><li>Building a Reverse Image Search Engine: Understanding Embeddings</li><li>From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy</li><li>Maximizing Speed and Performance of TensorFlow: A Handy Checklist</li><li>Practical Tools, Tips, and Tricks</li><li>Cloud APIs for Computer Vision: Up and Running in 15 Minutes</li><li>Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow</li><li>AI in the Browser with TensorFlow.js and ml5.js</li><li>Real-Time Object Classification on iOS with Core ML</li><li>Not Hotdog on iOS with Core ML and Create ML</li><li>Shazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit</li><li>Building the Purrfect Cat Locator App with TensorFlow Object Detection API</li><li>Becoming a Maker: Exploring Embedded AI at the Edge</li><li>Simulating a Self-Driving Car Using End-to-End Deep Learning with Keras</li><li>Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer</li></ol><div><div><b>Guest-contributed Content</b></div><div>The book features chapters from the following industry experts:</div><div><ul><li>Sunil Mallya (Amazon <b>AWS DeepRacer</b>)</li><li>Aditya Sharma and Mitchell Spryn (<b>Microsoft Autonomous Driving Cookbook</b>)</li><li>Sam Sterckval (<b>Edgise</b>)</li><li>Zaid Alyafeai (<b>TensorFlow.js</b>)</li></ul></div><div>The book also features content contributed by several industry veterans including François Chollet (<b>Keras</b>, <b>Google</b>), Jeremy Howard (<b>Fast.ai</b>), Pete Warden (<b>TensorFlow Mobile</b>), Anima Anandkumar (<b>NVIDIA</b>), Chris Anderson (<b>3D Robotics</b>), Shanqing Cai (<b>TensorFlow.js</b>), Daniel Smilkov (<b>TensorFlow.js</b>), Cristobal Valenzuela (<b>ml5.js</b>), Daniel Shiffman (<b>ml5.js</b>), Hart Woolery (<b>CV 2020</b>), Dan Abdinoor (<b>Fritz</b>), Chitoku Yato (<b>NVIDIA</b> Jetson Nano), John Welsh (<b>NVIDIA</b> Jetson Nano), and Danny Atsmon (<b>Cognata</b>).</div></div></div></div><div></div>