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 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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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 […]
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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 […]
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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 […]
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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 […]
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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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12 Machine Learning Books You Should Read in 2023
This complements the list that I posted earlier under the title “Math for Machine Learning: 14 Must-Read Books”, available here. Many of the following books have a free PDF version, their own website and GitHub repository, and usually you can purchase the print version. Some are self-published, with the PDF version regularly updated, and even […]
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New Book: Intuitive Machine Learning and Explainable AI
Intuitive Machine Learning with focus on explainable AI, human-friendly intelligence, powerful visualizations and applications. By Vincent Granville Ph.D, published in September 2022. PDF format, 156 pages. Version 1.0 with Python code. The book is available here. For my upcoming course based on this book, see here. This book covers the foundations of machine learning, with modern […]
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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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