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
Monday, April 25, 2022
Wednesday, March 16, 2022
Tuesday, March 15, 2022
- : Fractal supervised clustering in GPU (graphics processing unit) using image filtering techniques akin to neural networks, automated black-box detection of the number of clusters, unsupervised clustering in GPU using density (gray levels) equalizer.
- : New test of independence, spatial processes, model fitting, dual confidence regions, minimum contrast estimation, oscillating estimators, mixture and surperimposed models, radial cluster processes, exponential-binomial distribution with infinitely many parameters, generalized logistic distribution.
- : Statistical distribution of distances and Rayleigh test, Weibull distribution, properties of nearest neighbor graphs, size distribution of connected components, geometric features, hexagonal lattices, coverage problems, simulations, model-free inference.
- : Random functions, random graphs, random permutations, chaotic convergence, perturbed Riemann Hypothesis (experimental number theory), attractor distributions in extreme value theory, central limit theorem for stochastic processes, numerical stability, optimum color palettes, cluster processes on the sphere.
- here.: 28 exercises with solution expanding the theory and methods presented in the textbook, well documented source code and formulas to generate various deviates and simulations, simple recipes (with source code) to design your own data animations as MP4 videos - see ours on YouTube,
About the Author
How to Obtain the Book?
A different way to do regression with prediction intervals. In Python and without math. No calculus, no matrix algebra, no statistical eng...
Despite my long statistical and machine learning career both in academia and in the industry, I never heard of complex random variables unti...
In this article, original stochastic processes are introduced. They may constitute one of the simplest examples and definitions of point p...
The book is available on our e-store, here . View the table of contents, bibliography, index, list of figures and exercises here on my GitH...