The last Machine & Deep-Learning Compendium you’ll ever need

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This post was originally published by Ori Cohen at Towards Data Science

Photo Compendiums, Blende12, Pixabay.

A comprehensive resource on practically every topic for data science researchers

In the last 3 years, I have been curating everything related, directly or indirectly, to machine-learning (ML), deep-learning (DL), Statistics, Probability, NLP, NLU, deep-vision, etc. I started curating a compendium because I wanted to expand the scope of my knowledge. I believe that every researcher and data scientist (DS) should strive to learn more on a daily basis, not by hitting task-related walls and solving them, but as a lifelong learning practice. Personally, I read a few articles before I start my day, and once in a while I write and share the knowledge that I gain on Medium.

Partial table-of-contents, Part I, Dr. Ori Cohen’s Compendium

My COMPENDIUM is a ~330-page document, which I treat as my personal google for various summaries, links, and articles that I have read on every topic that interested me, or that I had needed to learn about. The topics include probably the majority of modern machine learning algorithms, feature selection and engineering techniques, deep-learning, NLP, audio, vision, time-series, anomaly detection; subjects such as hiring, managing, and experiment management, and much more. It will save you countless hours googling and sifting through articles that may not give you any value.

Partial table-of-contents, Part II, Dr. Ori Cohen’s Compendium

Please keep in mind that this is a perpetual work in progress, I constantly update it with new topics, so feel free to always visit. Finally, Feel free to give me feedback by using the comment option.

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This post was originally published by Ori Cohen at Towards Data Science

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