Computer Scientists' Cheatsheet
This documentation is a public note of any topic regards computer science. May it be Machine Learning, Discrete Mathematics or Bayesian Data Analysis.
These docs are mainly maintained by Seyoung Park.
- Mathematics for Machine Learning Linear Algebra by Imperial College London
- Introduction to Mathematical Thinking
- Linear Algebra by Khan academy
- Introduction to Probability and Statistics by MIT
.. toctree:: :maxdepth: 3 subjects/ai/index subjects/bayesian/index subjects/calculus/index subjects/computer_graphics/index subjects/deep_learning/index subjects/info-viz/index subjects/linear_algebra/index subjects/machine_learning/index subjects/papers/papers subjects/prob_stat/index subjects/Python/index subjects/unix/index
These docs are open source: all content is licensed under CC-BY 4.0 and all examples under CC0 (public domain). Additionally, this is an open project and we strongly encourage anyone to :doc:`contribute <README>`. For information, see the :doc:`README` and the Github links at the top of every page.
.. toctree:: :maxdepth: 1 README