MOTIVATION

Recent developments in deep learning have made once again made AI the "next big thing" in the tech community. Unfortunately, deep learning is also one area of AI that is perhaps among the most misunderstood by the media and general public alike. We feel that it is important to demystify deep learning as much as possible so that more people are educated about what it can and can't do, and how it can be used for good.

Each fall, we guide a cohort of members through our deep learning curriculum, complete with in-person workshops. Members will then have a chance to implement this content by working on a variety of team projects in the spring.

All of our curriculum resources (slide presentations, IPython notebooks, etc.) are publicly available for viewing in our Github. We recommend downloading and working through the incomplete notebooks from each lesson locally. You can then compare your work to the completed notebooks. We have also uploaded our full lectures onto our YouTube channel. We hope that these materials will serve as helpful resources for anyone wanting to learn more about deep learning and artificial intelligence.

(Note that deep learning/machine learning is only one part of the much larger field of artificial intelligence. If you are interested, we recommend you look into other topics in AI as well!)

OVERVIEW OF TOPICS

Lesson 1: Introduction to AI

AI is going to bring major shifts in society through developments in self-driving cars, medical image analysis, better medical diagnosis, and personalized medicine. But for many, it still remains a mystery.

NEXT STEPS

Special Topics

We’re compiling a list of some exciting topics within machine learning (and AI in general) that you may want to check out. Note that this is not by any means a comprehensive list of advanced topics in AI/machine learning — this is simply a list of active research areas that we think you should know about, and may hopefully serve as a valuable starting point for branching out into more specific fields.

Sections

Part 1: Transfer Learning Part 2: Reinforcement Learning
Reading List

We're compiling a list of interesting and influential papers that we recommend you check out, if you're interested.