Stochastic Processes, 2nd Edition, now with Python Code
The book covers supervised classification, including fractal classification, as well as unsupervised clustering, using an innovative approach. Datasets are first mapped onto an image, then processed using image filtering techniques. I discuss the analogy with neural networks, comparing very deep but sparse neural networks, with standard networks. Topics Covered The book covers many aspects of […]
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Fast Classification and Clustering via Image Convolution Filters
Subtitled “Alternative to Generative Mixture Models”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. It is also described in details in my book “Stochastic Processes and Simulations: A Machine Learning Perspective”, available here. I explain, with Python code and numerous illustrations, how to turn traditional tabular data into images, […]
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Math for Machine Learning: 14 Must-Read Books
It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here. Interestingly, this latter math-free machine […]
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The Art of Visualizing High Dimensional Data
Entitled “The Art of Visualizing High Dimensional Data”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. Also discussed in details with Python code in chapter 4 in my book “Intuitive Machine Learning and Explainable AI”, available here. This article discusses enriched visualizations, with a focus on animated gifs and videos […]
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