An aggregation of resources for studying and keeping up to date with Machine Learning and mathematics.
- https://www.coursera.org/learn/machine-learning
- https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/
- http://ai.berkeley.edu/home.html
- http://www.paulgraham.com/
- https://jeremykun.com/
- https://opensource.googleblog.com/
- http://colah.github.io/
- http://cs.stanford.edu/people/karpathy/
- http://blog.stephenwolfram.com/
- https://developers.googleblog.com/
- http://technews.acm.org/
- https://news.ycombinator.com/
- https://www.kdnuggets.com
- https://www.nextplatform.com/category/analyze/
- https://twimlai.com/
- http://technews.acm.org/
- Artificial Intelligence: A Modern Approach - http://aima.cs.berkeley.edu/
- Computer Vision: Models, Learning, and Inference - http://www.computervisionmodels.com/
- Pattern Recognition and Machine Learning - https://www.microsoft.com/en-us/research/people/cmbishop/
- The Elements of Statistical Learning - https://web.stanford.edu/~hastie/ElemStatLearn/
- The Deep Learning Book - http://www.deeplearningbook.org/
- Cornell Machine Learning - http://machinelearning.cis.cornell.edu/index.php
- Oxford Information Engineering - http://www.robots.ox.ac.uk/
- Harvard Intelligent Probabilistic Systems - http://hips.seas.harvard.edu/
- MIT CS and AI - http://www.csail.mit.edu/
- The Great Danbury AI Playlist - https://www.youtube.com/playlist?list=PLXHD0TtFzS8s1sb14dRKAi5mRWFIl2G1Q
- Visualization
- Scientific Computing
- NLP
- TensorFlow
- Boltzmann Machines in TensorFlow with examples - https://github.com/monsta-hd/boltzmann-machines
- https://github.com/lengstrom/fast-style-transfer
- https://github.com/vahidk/EffectiveTensorflow
- https://github.com/jtoy/awesome-tensorflow
- Deep Learning Book Study Group - https://github.com/DanburyAI/SG_DLB_2017