2nd Edition of My Book Now Published, 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. Sponsors The free distribution of our content would […]
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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. I explain, with Python code and numerous illustrations, how to turn traditional tabular data into images, to perform both clustering and supervised classification using simple image filtering techniques. I also explain how to generalize the […]
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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. This article discusses enriched visualizations, with a focus on animated gifs and videos built in Python. For instance, the comet video displayed below can feature several dimensions that are difficult to show in […]
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Gentle Introduction to Linear Algebra, with Spectacular Applications
This is not a traditional tutorial on linear algebra. The material presented here, in a compact style, is rarely taught in college classes. It covers a wide range of topics, while avoiding excessive use of jargon or advanced math. The fundamental tool is the power of a matrix, and its byproduct, the characteristic polynomial. It […]
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Fuzzy Regression: A Generic, Model-free, Math-free Machine Learning Technique
Some people climb Mount Everest solo in winter, with no oxygen. Some mathematicians prove difficult theorems using only elementary arithmetic. The proof, despite labeled as “elementary” is typically far more complicated than those based on advanced mathematical theory. The people accomplishing these feasts are very rare. Introduction For years, I have developed machine learning techniques […]
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New Book: Approaching (Almost) Any Machine Learning Problem
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, here. The most recent version, dated June 2021, is available in PDF format, here. This is not a traditional book. It feels like a repository of Python code, […]
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New Book: Interpretable Machine Learning
Subtitled “A Guide for Making Black Box Models Explainable”. Authored and self-published by Christoph Molnar, 2022 (319 pages). This is actually the second edition, the first one was published in 2019. According to Google Scholar, it was cited more than 2,500 times. So this is a popular book about a popular topic. General Comments The […]
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New Book: Efficient Deep Learning
Subtitled “Fast, smaller, and better models”. This book goes through algorithms and techniques used by researchers and engineers at Google Research, Facebook AI Research (FAIR), and other eminent AI labs to train and deploy their models on devices ranging from large server-side machines to tiny microcontrollers. The book presents a balance of fundamentals as well […]
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Little Known Secrets about Interpretable Machine Learning on Synthetic Data
This first article in a new series on synthetic data and explainable AI, focuses on making linear regression more meaningful and controllable. Includes synthetic data, advanced machine learning with Excel, combinatorial feature selection, parametric bootstrap, cross-validation, and alternatives to R-squared to measure model performance. The full technical article (PDF, 13 pages, with detailed explanations and […]
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