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Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
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About Practical Deep Learning For Cloud, Mobile, And
Product 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. 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). 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 c
