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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning
| Date | Stars |
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| 2026-07-31 | 624 |
| 2026-08-03 | 624 |
| 2026-08-04 | 624 |
| 2026-08-06 | 624 |
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*This list can be found on github and medium: https://github.com/memo/ai-resources https://medium.com/@memoakten/selection-of-resources-to-learn-artificial-intelligence-machine-learning-statistical-inference-23bc56ba655* ***Update April 2017**: It’s been almost a year since I posted this list of resources, and over the year there’s been an explosion of articles, videos, books, tutorials etc on the subject — even an explosion of ‘lists of resources’ such as this one. It’s impossible for me to keep this up to date. However, the one resource I would like to add is https://ml4a.github.io/ (https://github.com/ml4a) led by Gene Kogan. It’s specifically aimed at artists and the creative coding community.* # Introduction This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials *that I have personally completed* so that I can include brief personal comments on them. It is also by no means the *best* path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy). As someone who has no formal background in Computer Science (but has been programming for many years), the language, notation and concepts of ML/SI/DL and even CS was completely alien to me, and the learning curve was not only steep, but vertical, treacherous and slippery like ice. A lot of the resources below are actually not for DL but more comprehensive ML/SI. DL is mostly just tweaks on top of older techniques, so once you have a solid foundation in ML/SI it makes a lot more sense. If you go through the video lectures below (including advanced ones), you'll be able to pick up current DL developments directly from the published papers. If you really want to understand AI/ML/SI/DL/RL indepth with all the maths, you need a good understanding of linear algebra (vectors & matrices), probability and statistics (which is more complex than it sounds), and calculus (mainly multivariate differential calculus, which is often simpler than it sounds). I've included lectures for these too. You can't cut corners. Take the time and study as much of the below as you can, from the beginning. Strong foundations are crucial. I often started watching one lecture, 5 minutes in I realized I didn't understand anything so went back to watch another lecture which covered slightly more fundamental topics, 5 minutes in I realized I still didn't understand anything so went back to watch another lecture which covered even more fundamental topics, etc. until I went back 10 lectures. It has been depressing at times (like trying to climb vertical, treacherous, slippery walls of ice without the right tools). If you just want to *use* the algorithms without necessarily understanding or delving into the maths, or just want to understand the algorithms at a high conceptual level, that's perfectly fine too. Hopefully my comments below will make it clear what's what. # Tips There's a lot of overlap in the lectures below. That's a **good** thing. Don't skip things because you've already read or seen them elsewhere. If you're trying to learn and *understand* something which is potentially quite complicated, having different people explain the same thing to you in different ways is very useful and often gives insight or intuition you might not otherwise find. If there are sections which you are 100% comfortable with, then yo
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