Jared Lander is the Founder and CEO of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts. He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike.

Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience.

Michael Osborne, Dyson Associate Professor in Machine Learning, University of Oxford "This book should be the first stop for deep learning beginners, as it contains lots of concrete, easy-to-follow examples with corresponding tutorial videos and code notebooks.