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.
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 contains various summaries, links, and articles that I have read on numerous topics that I found interesting or that I had needed to learn. It include 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.
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 mlcompendium.com website or directly access the compendium “book”. As seen in Figure1, 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.
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.
Continue reading: https://towardsdatascience.com/the-machine-deep-learning-compendium-open-book-7e7bd77fbc4f?source=rss—-7f60cf5620c9—4