The Machine & Deep Learning Compendium
Hi! When I announced 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.
PLEASE NOTE: I'm in the midst of restructuring the compendium. I'm taking on this huge effort because the compendium started as my personal list of resources for my own private use without any standardization and without the intention of opening it up for the public, in order to allow readers to easily find topics and for referenced authors to get support back from the community. If you feel like something should be changed, content-wise, please create a PR or contact me and we'll work together to make that happen. My intent is to support both the community and authors and to democratise education.
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.