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

Description

** 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. 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).• •