The Machine & Deep Learning Compendium
Hi! Nearly a year ago I announced the Machine & Deep Learning Compendium, a Google document that I have been curating for the last 4 years. The ML Compendium contains over 500-topics, and in its document version, it was over 400 pages long.
The Compendium is fully open. It is now a project on GitBook & GitHub (please star it!). I believe in knowledge sharing and the compendium will always be free to everyone.
I see this compendium as a gateway, as a frequently visited resource for people of various proficiency levels, for industry data scientists, and academics. The compendium will save you countless hours googling and sifting through articles that may not give you any value.
The Compendium includes around 500 topics, that contain various summaries, links, and articles that I have read on numerous topics that I found interesting or that I had needed to learn. It includes the majority of modern machine learning algorithms, statistics, feature selection, and engineering techniques, deep-learning, NLP, audio, deep & classic vision, time-series, anomaly detection, graphs, experiment management, and much more. In addition to strategic topics such as data science management and team building, and essential topics such as product management, product design, and a technology stack from a DS POV.
Please keep in mind that this is a perpetual work in progress with a variety of topics. If you feel that something should be changed, you can now easily contribute using GitBook, GitHub, or contact me.
Please note that on GitHub you should push to master.
I would like to thank the following contributors: Samuel Jefroykin, Sefi Keller​
Last modified 12d ago
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