Gold BlogThe Machine & Deep Learning Compendium Open Book

After years in the making, this extensive and comprehensive ebook resource is now available and open for data scientists and ML engineers. Learn from and contribute to this tome of valuable information to support all your work in data science from engineering to strategy to management.

By Ori Cohen, AI/ML/DL Expert, Researcher, Data Scientist.

Partial Topic List From The Machine & Deep Learning Compendium.

Nearly a year ago, I announced the Machine & Deep Learning Compendium, a Google document that I have been writing for the last 4 years. The ML Compendium contains over 500 topics, and it is over 400 pages long.

Today, I’m announcing that the Compendium is fully open. It is now a project on GitBook and 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 and classic vision, time series, anomaly detection, graphs, experiment management, and much more. In addition, strategic topics, such as data science management and team building, are highlighted as well as other essential topics, such as product management, product design, and a technology stack from a data science perspective.

Please keep in mind that this is a perpetual work in progress on a variety of topics. If you feel that something should be changed, you can now easily contribute using GitBook, GitHub, or contact me.


The ML Compendium is a project on GitBook, which means that you can contribute as a GitBook writer. Writing and editing content using the internal editor is easy and intuitive, especially compared to the more advanced option of contributing via GitHub pull requests.

You can visit the website or directly access the compendium “book”. As seen in Figure 1, on the left you have the main topics and on the right the sub-topics which are in each main topic, not to mention that the search feature is more advanced, especially compared to the old method of using CTRL-F inside the original document.

Figure 1: The Machine & Deep Learning Compendium with the GitBook UI.

The following are two topics that may interest you, the natural language processing (NLP) page, as seen in Figure 2, and the deep neural nets (DNN) page, as seen in Figure 3.

Figure 2: Natural Language Processing.

Figure 3: Deep Neural Nets.


Alternatively, you can use GitHub (Figure 4) if you want to contribute content, please place the content within the proper topic, then create a pull request to a new branch. Finally, don't forget to ‘Star’ the project if you like it.

The following is a simple set of instructions for contributing using GitHub:

  1. git clone
  2. git branch mybranch
  3. git switch mybranch
  4. add your content
  5. git add the-edited-file
  6. git commit -m “my content”
  7. git push
  8. create a PR by visiting this link:

Figure 4: The GitHub project.


Original. Reposted with permission.


Bio: Dr. Ori Cohen has a Ph.D. in Computer Science with a focus on machine learning. He is the author of the ML & DL Compendium and the He is a lead data scientist at New Relic TLV, doing machine and deep learning research in the field of AIOps & MLOps.