Explainable AI Featured Posts Generative AI Synthetic Data

Generative AI Technology Break-through: Spectacular Performance of New Synthesizer

Neural network methods have overshadowed all other techniques in the last decade, to the point that alternatives are simply ignored. And for good reasons: techniques such as generative adversarial networks (GAN) proved very successful in some contexts, especially computer vision. Indeed, there has been several attempts to turn every problem and traditional method — regression, […]

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Data Sets Explainable AI Featured Posts Generative AI Synthetic Data Time Series

Synthesizing Geospatial Data with A Simple NoGAN Technique

If you regularly read my articles, you know that I developed several different techniques for data synthetization. Many are explained in details in my upcoming book Synthetic Data and Generative AI (Elsevier), available here. It includes generative adversarial networks (GANs), copulas, agent-based modeling, methods based on interpolation, correlated noise mixtures, and more. The technique presented […]

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Data Sets Experimental Math Featured Posts Generative AI Synthetic Data Time Series

Sound Generation in Python: Turning Your Data into Music

Not long ago, I published here an article entitled “The Sound that Data Makes”. The goal was turning data — random noise in this case — into music. The hope was that by “listening” to your data, you could gain a different kind of insights, not conveyed by visualizations or tabular summaries. This article is […]

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Featured Posts Generative AI Machine Learning Synthetic Data

Generated Data vs Monte-Carlo Simulations: What are the Differences?

I sometimes get asked this question: could you use simulations instead of synthetizations? Below is my answer, also focusing on some particular aspects of data synthetizations, that differentiate them from other techniques. Simulations do not simulate joint distributions Sure, if all your features behave like a mixture of multivariate normal distributions, you can use GMMs […]

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Books Featured Posts Generative AI Python Synthetic Data

My Book on Generative AI Now on Amazon

Published by Elsevier, available in print in January 2024. You can pre-order it now, here. The PDF version is available on my e-store. The book is fully written. It is offered at a steep discount to participants in my Generative AI certification program, available here. This is the first technical book focusing on synthetic data and its […]

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Data Sets Featured Posts Synthetic Data

Generative AI: Synthetic Data Vendor Comparison and Benchmarking Best Practices

The goal of data synthetization is to produce artificial data that mimics the patterns and features present in existing, real data. Many generation methods and evaluation techniques are available, depending on purposes, the type of data, and the application field. Everyone is familiar with synthetic images in the context of computer vision, or synthetic text […]

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High-value AI and Machine Learning Certifications Under $50

Our AI/ML research lab now offers a quick path to certification in generative AI and other modern topics relevant to new developments in the industry, as well as traditional and specialized certifications and training. All in Python. Probably the fastest and most affordable way to earn professional, high value credentials offered by one of the […]

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Featured Posts Machine Learning Statistical Science Stochastic Systems Synthetic Data Time Series

A Synthetic Stock Exchange Played with Real Money

Not only that, but you can predict — more precisely compute with absolute certainty — what the value of any stock will be tomorrow. Transaction fees are well below 0.05% and the market, at least in the version presented here, is fair: in other words, a zero-sum game if you play by luck. If instead […]

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Computer Vision Explainable AI Featured Posts Machine Learning Synthetic Data

Autonomous Driving: Boosting Optical Flow with Synthetic Data

Optical flow is defined as the task of estimating per-pixel motion between video frames. Optical flow models take two sequential frames as input and return as output a flow vector that predicts where each pixel in the first frame will be in the second frame. Optical flow is an important task for autonomous driving, but […]

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Featured Posts Machine Learning Stochastic Systems Synthetic Data Visualization

Generating and Videolizing Agglomerative Processes

This short article explains how to efficiently simulate the evolution of agglomerative processes, and visualize their behavior with data animations. I use a generic, simple model for illustration purposes: atoms, initially consisting of one electron, collide and merge over time, with a pre-specified maximum number of electrons per atom: the maximum limit. Given enough time, […]

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