Let's study together.
Practical Deep Learning For Coders (http://course.fast.ai/), is a new course, taught by Jeremy Howard (Kaggle's (http://www.kaggle.com/) #1 competitor 2 years running, and founder of Enlitic (http://www.enlitic.com/)). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down. Oh and one other thing... it's totally free! (but you might end up paying AWS about $20 to use their GPU servers)
Come on down to ask and answer questions if you get stuck working through the labs.
They say the best way to learn is to teach. So I've made it through the first three weeks and am happy to share what I learned. Everyone is welcome.
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. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF (https://www.usfca.edu/data-institute/). Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.