Tuesday, May 10, 2022

How to Create/Use Great Synthetic Data for Interpretable Machine Learning

I share here my new article on synthetic data and interpretable machine learning. It will show you how to set up such an environment. I also mention three popular books published in the last three months. The figure below is from the first article featured in this newsletter.

Article: synthetic data and interpretable machine learning. 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 […]. Read more here.

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 […]. Read my review here.

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 […] Read more here.

New book: Probabilistic Machine Learning. By Kevin Murphy, MIT Press (2022). This is one of the best machine learning books that I purchased in the last few years. Very comprehensive, covering a lot of statistical science too. The level is never too high, despite a few advanced concepts being discussed. There is a lot of focus on applications, especially image […] Read my review here.

Browse the MLTechniques.com blog by category to find more content that is relevant to you. For instance, articles in the synthetic data category can be found here. The resources section, here, features detailed technical reports and other books, some available to subscribers only, some available to all.

No comments:

Post a Comment

Note: Only a member of this blog may post a comment.

Fuzzy Regression: A Generic, Model-free, Math-free Machine Learning Technique

  A different way to do regression with prediction intervals. In Python and without math. No calculus, no matrix algebra, no statistical eng...