moocs.md

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hackpack	MOOCs	title author link tags desc fast.ai ~ DL 2 Jeremy Howard & Rachel Thomas https://course.fast.ai/part2.html fast.ai free Similar to their Deep Learning (Part 1) course, fast.ai's Deep Learning (Part 2) takes a deep dive into learning how to work with cutting-edge algorithms and in highly technical problem spaces. title author link tags desc fast.ai ~ DL 1 Jeremy Howard & Rachel Thomas http://course.fast.ai/ fast.ai free Similar to their Machine Learning course, fast.ai's Deep Learning (Part 1) course goes through a variety of Neural Network models and develops an understanding of how these algorithms work, both on an algorithmic and a computational level. title author link tags desc fast.ai ~ ML Jeremy Howard & Rachel Thomas http://course.fast.ai/ml.html fast.ai free This is a free MOOC taught in a different way than most. Jeremy and Rachel start with the cool stuff you can do then peel back the layers, rather than starting from zero teaching you to build up. They also teach code-centric, which means you're learning how to build these algorithms as you go rather than strictly developing an understanding. title author link tags desc CS188 ~ Intro to AI UCBerkeley ai.berkeley.edu Berkeley This is Berkeley's undergrad Intro to AI course. It covers everything from Uninformed Search (what you learn in Computer Science 2) to Markov Decision Processes, Reinforcement Learning, all the way through to Bayes' Nets, and some applications of Machine Learning. title author link tags desc Machine Learning Coursera x Stanford coursera.org/learn/machine-learning Coursera Stanford Andrew Ng takes you through a more theoretical understanding of Machine Learning, with Coursera's first course. Covering Supervised versus Unsupervised Learning as well as some best practices, it's all around a pretty informative class. title author link tags desc Deep Learning Coursera coursera.org/specializations/deep-learning Coursera This is an all-around set of courses; it's more involved than the Machine Learning course on Coursera, but covers way more. Ultimately, you'll need to find the separate courses on Coursera to get all the content at no charge. title author link tags desc CS321n ~ Convolutional Neural Networks for Visual Recognition Stanford cs231n.github.io Stanford YouTube Fei Lei runs the Computer Vision Lab at Stanford, and her former Ph.D student Andrej Karpathy is really making a name for himself in AI. He also happens to have an awesome understanding of Neural Networks and can convey that understanding quite well. They have the lectures on YouTube, but the write-ups are also great because of their detail.

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