Deep Learning — the use of neural networks with modern techniques to tackle problems ranging from computer vision to speech recognition and synthesis — is certainly a current buzzword. However, at the core is a set of powerful methods for organizing self-learning systems. Multi-layer neural networks aren’t new, but there is a resurgence of interest primarily due to the availability of massively parallel computation platforms disguised as video cards.
The problem is getting started in something like this. There are plenty of scholarly papers that can be hard to wade through. Or you can grab some code from GitHub and try to puzzle it out.
A better idea would be to take a free class entitled: Practical Deep Learning for Coders, Part 1. The course is free unless you count your investment in time. They warn you to expect to commit about ten hours a week for seven weeks to complete the course. You can see the first installment in the video, below.
The course originated at the University of San Francisco. Here’s their description:
This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.
Lesson 1 covers distinguishing cats from dogs. There’s a Slack channel for chat, a forum, and other support resources. You might be concerned about where part 2 is. According to the site, it should be available online in May of 2017.