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 […]
Read More
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, […]
Read More
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 […]
Read More
Data Synthetization Explained in One Picture
The diagram is organized as follows. Dashed blue lines are associated to GANs (generative adversarial networks), where the goal is to produce a sequence of synthetic datasets that get better and better at mimicking the structure present in the real data, over successive iterations. The diagram features 5 such iterations, with the synthetized datasets denoted […]
Read More
Introduction to Discrete Chaotic Dynamical Systems
Entitled “Introduction to Discrete Chaotic Dynamical Systems”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. This article is an extract from my book “Gentle Introduction to Chaotic Dynamical Systems”, available here. This is chapter 2 of my upcoming book on dynamical systems and related stochastic processes, expected to be […]
Read More
Random Walks, Brownian Motions, and Related Stochastic Processes
Entitled “Random Walks, Brownian Motions, and Related Stochastic Processes”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. This article is an extract from my book “Gentle Introduction to Chaotic Dynamical Systems”, available here. In about 15 pages, this scratch course covers a lot more material than expected in such […]
Read More
New Book on Synthetic Data: Version 3.0 Just Released
Update on March 3, 2023: Version 4.0 has been released and now replaces version 3.0 on the e-Store. It contains a new full chapter on enhanced generative adversarial networks (GANs) with comparison to copula-based methods for data synthetization, with illustrations on real-life datasets. The book has considerably grown since version 1.0. It started with synthetic […]
Read More
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 […]
Read More
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 […]
Read More
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 […]
Read More
You must be logged in to post a comment.