Month: March 2023

Featured Posts Machine Learning Statistical Science Synthetic Data

Smart Grid Search for Faster Hyperparameter Tuning

The objective is two-fold. First, I introduce a 2-parameter generalization of the discrete geometric and zeta distributions. Indeed, a combination of both. It allows you to simultaneously match the variance and mean in observed data, thanks to the two parameters p and α. To the contrary, each distribution taken separately only has one parameter, and […]

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Books Experimental Math Featured Posts Statistical Science Stochastic Systems

New Book: Gentle Introduction To Chaotic Dynamical Systems

In less than 100 pages, the book covers all important topics about discrete chaotic dynamical systems and related time series and stochastic processes, ranging from introductory to advanced, in one and two dimensions. State-of-the art methods and new results are presented in simple English. Yet, some mathematical proofs appear for the first time in this […]

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Data Sets Featured Posts Machine Learning

Feature Clustering: A Simple Solution to Many Machine Learning Problems

Feature clustering is an unsupervised machine learning technique to separate the features of a dataset into homogeneous groups. In short, it is a clustering procedure, but performed on the features rather than on the observations. Such techniques often rely on a similarity metric, measuring how close two features are to each other. In this article, […]

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Books Data Sets Deep Learning Explainable AI Featured Posts Machine Learning Synthetic Data

Data Synthetization: enhanced GANs vs Copulas

Using case studies, I compare generative adversarial networks (GANs) with copulas to synthesize tabular data. I discuss back-end and front-end improvements to help GANs better replicate the correlation structure present in the real data. Likewise, I discuss methods to further improve copulas, including transforms, the use of separate copulas for each population segment, and parametric […]

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