Author: Vincent Granville

Vincent Granville is a pioneering data scientist and machine learning expert, founder of MachineLearningRecipes.com, co-founder of Data Science Central (acquired by TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent's past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS). Vincent published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is also the author of multiple books, available here. He lives in Washington state, and enjoy doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.
Experimental Math Featured Posts Machine Learning Visualization

Math-free, Parameter-free Gradient Descent in Python

Entitled “Math-free, Parameter-free Gradient Descent in Python”, the full version in PDF format is accessible in the “Free Books and Articles” section, here.  I discuss techniques related to the gradient descent method in 2D. The goal is to find the minima of a target function, called the cost function. The values of the function are computed at […]

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Data Sets Featured Posts Machine Learning Synthetic Data Time Series Visualization

New Interpolation Methods for Data Synthetization and Prediction

Entitled “New Interpolation Methods for Synthetization and Prediction”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. This article is an extract from my book “Synthetic Data and Generative AI”, available here. I describe little-known original interpolation methods with applications to real-life datasets. These simple techniques are easy to implement and can […]

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Data Sets Featured Posts Machine Learning Synthetic Data

Synthetizing the Insurance Dataset Using Copulas: Towards Better Synthetization

This article is an extract from my book “Synthetic Data and Generative AI”, available here. In the context of synthetic data generation, I’ve been asked a few times to provide a case study focusing on real-life tabular data used in the finance or health industry. Here we go: this article fills this gap. The purpose is […]

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Data Sets Featured Posts Machine Learning Statistical Science Synthetic Data

Military-grade Fast Random Number Generator Based on Quadratic Irrationals

This article is an extract from my book “Synthetic Data and Generative AI”, available here. There are very few serious articles in the literature dealing with digits of irrational numbers to build a pseudo-random number generator (PRNG). It seems that this idea was abandoned long ago due to the computational complexity and the misconception that such […]

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Explainable AI Featured Posts Machine Learning Synthetic Data Visualization

Empirical Optimization with Divergent Fixed Point Algorithm – When All Else Fails

Entitled “Empirical Optimization with Divergent Fixed Point Algorithm – When All Else Fails”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. Also discussed in details with Python code in my book “Synthetic Data”, available here. While the technique discussed here is a last resort solution when all else fails, it is […]

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Explainable AI Featured Posts Podcasts Synthetic Data

Podcast: Synthetic Data and Generative AI – Importance, Misconceptions and Applications

Fireside chat: Synthetic data – importance, misconceptions and applications In this video, Vincent talks about how synthetic data can be leveraged across various industries to enhance predictions and test blackbox systems leading to more fairness and transparency in AI. Hosted by Victor Chima, co-founder at Learncrunch.com. Topics discussed include: To learn more about synthetic data […]

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Courses Explainable AI Featured Posts Machine Learning Synthetic Data Visualization

Course: Synthetic Data and Interpretable Machine Learning

This live course is based on my book “Synthetic Data”, available here. Participants will receive a free copy of this book. The information below provides a brief overview of the course. For enrollment or inquiries, follow this link. Course Description The performance of machine learning algorithms such as classification, clustering, regression, decision trees or neural networks […]

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Books Computer Vision Explainable AI Featured Posts Machine Learning Natural Language Processing Stochastic Systems Synthetic Data Time Series Visualization

New Book: Synthetic Data and Generative AI

Synthetic data is used more and more to augment real-life datasets, enriching them and allowing black-box systems to correctly classify observations or predict values that are well outside of training and validation sets. In addition, it helps understand decisions made by obscure systems such as deep neural networks, contributing to the development of explainable AI. […]

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Computer Vision Data Sets Explainable AI Featured Posts Machine Learning Synthetic Data Visualization

Spectacular Videos: Synthetic Universes, with Star Collision Graph

Entitled “Spectacular Videos: Synthetic Universes, with Star Collision Graph”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. Also discussed in details with Python code in my book “Synthetic Data”, available here. This project started as an attempt to generate simulations for the three-body problem in astronomy: studying the orbits of three […]

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Computer Vision Explainable AI Featured Posts Machine Learning Stochastic Systems Synthetic Data Visualization

Dynamic Clouds and Landscape Generation: Morphing and Evolutionary Processes

Entitled “Dynamic Clouds and Landscape Generation: Morphing and Evolutionary Processes”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. Also discussed in details with Python code in my book “Synthetic Data”, available here. My previous article focused on map generation in 3D, and also features a fascinating video, see here. In this […]

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