Hi! When I created the Machine & Deep Learning Compendium, it was a personal list of resources curated in a private Google document, for my own education. That document is now retired in favor of this new interface. I decided to share it as an educational tool in order to allow people to learn and connect to all the great authors that I summarized, quoted, and referenced.
The Compendium is fully open. It is now a project on GitBook & GitHub (please star it!). I believe in education and knowledge sharing and the compendium will always be not-for-profit and free. 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, and for reaching great authors that you can support further.
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.