A different way to do regression with prediction intervals. In Python and without math. No calculus, no matrix algebra, no statistical engineering, no regression coefficients, no bootstrap. Multivariate and highly non-linear. Interpretable and illustrated on synthetic data. Read more here.
Sunday, May 22, 2022
Tuesday, May 17, 2022
This self-published book is dated July 2020 according to Amazon. But it appears to be an ongoing project. Like many new books, the material is on GitHub. The most recent version, dated June 2021, is available in PDF format.
This is not a traditional book. It feels like a repository of Python code, printed on paper if you buy the print version. The associated GitHub repository is much more useful if you want to re-use the code with simple copy and paste. It covers a lot of topics and performance metrics, with emphasis on computer vision problems. The code is documented in details. The code represents 80% of the content, and the comments in the code should be considered as an important, integral part of the content.
A Non-traditional Book
That said, the book is not an introduction to machine learning algorithms. It assumes some knowledge of the algorithms discussed, and there is no mathematical explanations. I find it to be an excellent 300-page Python tutorial covering many ML topics (maybe too many). The author focuses on real problems and real data. The style is very far from academic, and in my opinion, anti-academic.
Read the full review, see table of contents, and get the book, here.
Tuesday, May 10, 2022
A different way to do regression with prediction intervals. In Python and without math. No calculus, no matrix algebra, no statistical eng...
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This self-published book is dated July 2020 according to Amazon. But it appears to be an ongoing project. Like many new books, the material ...
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