Part 1. Foundational methods

Deep learning seems to be very new. Self-driving cars, computerized personal assistants, and chat bots are permeating our society—the applications and real-world impact of deep learning seemingly went from zero to ubiquitous in under a decade. So you want to dive right in and learn how to make your own self-driving robotic vacuum that will tell you where your cat hides the rubber bands and the socks that keep disappearing.

But if you really want to understand deep learning, you can’t just drink from the firehose. The foundations are over six decades old—a tree whose fruit has finally ripened. You need to learn the basic techniques that serve as the foundation of modern deep learning, which we can then build on to grow your knowledge.