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

Anirudh Koul is a data scientist at Microsoft. He brings eight years of applied research experience on petabyte-scale social media datasets including Facebook, Twitter, Yahoo Answers, Quora, Foursquare, and Bing. He has worked on a variety of machine learning, natural language processing, and information retrieval-related projects at Yahoo, Microsoft, and Carnegie Mellon University. Rapidly prototyping ideas, he has won over two dozen innovation, programming, and 24 hour-hackathon contests organized by companies including Facebook, Google, Microsoft, IBM, and Yahoo. Koul was also the keynote speaker at the SMX conference in Munich (March 2014), where he spoke about trends in applying machine learning on big data. You can read more about him here: linkedin.com/in/anirudhkoul

Siddha, a Data Scientist at Deep Vision, is applying deep learning on embedded devices and low power devices. She is a graduate from Carnegie Mellon University with a Master's in Computational Data Science where she worked on solving problems that connect natural languages and computer vision using deep learning and machine learning. Her work spans Visual Question Answering, Generative Adversarial Networks, gathering insights from CERN's petabyte scale data, and has been published at top tier conferences like CVPR. She is a frequent speaker at the Strata Data and the O’Reilly AI conferences and advises the AI Data Lab (FDL) at NASA. She has been the 2013 Indian Youth Women's Representative for the Institute of Engineering and Technology, a 2015 Grace Hopper Scholar and is a member of the Open Leadership Cohort, Mozilla Science Lab.