Practical Deep Learning for Cloud and Mobile

Whether you're a software engineer aspiring to enter the world of artificial intelligence, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where do I begin? This step-by-step guide teaches you how to build practical applications using deep neural networks for the cloud and mobile using a hands-on approach. 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 can use in the real world. Train, optimize, and deploy computer vision models with Keras, TensorFlow, CoreML, TensorFlow Lite, and MLKit, rapidly taking your system from zero to production quality. Develop AI applications for the desktop, cloud, smartphones, browser, and smart robots using Raspberry Pi, Jetson Nano, and Google Coral Perform Object Classification, Detection, Segmentation in real-time Learn by building examples such as Silicon Valley's "Not Hotdog" app, image search engines, and Snapchat filters Train an autonomous car in a video game environment and then build a real mini version Use transfer learning to train models in minutes Generate photos from sketches in your browser with Generative Adversarial Networks (GANs with pix2pix), and Body Pose Estimation (PoseNet) Discover 50+ practical tips for data collection, model interoperability, debugging, avoiding bias, and scaling to millions of users