Machine Learning Techniques

Books Deep Learning Featured Posts Generative AI Machine Learning Python

New Book: Understanding Deep Learning

By Simon Prince, computer science Professor at the University of Alberta. To be published by MIT Press, Dec 2023. The author shares the associated Jupyter notebooks on his website, here. Very popular, it got over 5,000 likes when the author announced the upcoming book on LinkedIn. I pre-ordered my copy. Summary An authoritative, accessible, and […]

Explainable AI Featured Posts Generative AI Podcasts Synthetic Data

NoGAN: Ultrafast Data Synthesizer – My Talk at ODSC San Francisco

My talk at the ODSC Conference, San Francisco, October 2023. Includes Notebook demonstration, using our open-source Python libraries. View or download the PowerPoint presentation, here. I discuss NoGAN, an alternative to standard tabular data synthetization. It runs 1000x faster than GAN, consistently delivering better results according to the most sophisticated evaluation metric, implemented here for […]

Experimental Math Featured Posts Machine Learning Python Statistical Science

Quantum Derivatives, GenAI, and the Riemann Hypothesis

Have you ever encountered a function or cumulative probability distribution (CDF) that is nowhere differentiable, yet continuous everywhere? Some are featured in this article. For a CDF, it means that it does not have a probability density function (PDF), and for a standard function, it has no derivative. At least, not until now. The quantum […]

Data Sets Experimental Math Featured Posts Python Statistical Science

Number Theory: Longest Runs of Zeros in Binary Digits of Square Root of 2

Studying the longest head runs in coin tossing has a very long history, starting in gaming and probability theory. Today, it has applications in cryptography and insurance. For random sequences or Bernoulli trials, the associated statistical properties and distributions have been studied in detail, even when the proportions of zero and one are different. Yet, […]

Books Explainable AI Featured Posts Generative AI Synthetic Data

New Book: Statistical Optimization for Generative AI and Machine Learning

With case studies, Python code, new open source libraries, and applications of the GenAI game-changer technology known as NoGAN (194 pages). This book covers optimization techniques pertaining to machine learning and generative AI, with an emphasis on producing better synthetic data with faster methods, some not even involving neural networks. NoGAN for tabular data is […]

Explainable AI Featured Posts Generative AI Machine Learning Podcasts Synthetic Data

NoGAN: New Generation of Synthetic Data (Video)

My talk at the Generative AI Conference, London, September 2023. View or download the PowerPoint presentation, here. I introduce a new, NoGAN alternative to standard tabular data synthetization. It is designed to run faster by several orders of magnitude, compared to training generative adversarial networks (GAN). In addition, the quality of the generated data is […]

Explainable AI Featured Posts Generative AI Python Statistical Science Synthetic Data

GenAI: Fast Data Synthetization with Distribution-free Hierarchical Bayesian Models

Deep learning models such as generative adversarial networks (GAN) require a lot of computing power, and are thus expensive. Also, they may not convergence. What if you could produce better data synthetizations, in a fraction of the time, with explainable AI and substantial cost savings? This is what Hierarchical Deep Resampling was designed for. It […]

Data Sets Explainable AI Featured Posts Generative AI Machine Learning Synthetic Data

New Python Library to Evaluate AI-generated Data and Compare Models

Called GenAI-Evalution, you use it for instance to assess the quality of tabular synthetic data. In this case, it measures how faithfully the synthetization mimics the real data it is derived from, by comparing the full joint empirical distributions (ECDF) attached to the two datasets. It works both with categorical and numerical features, and returns […]

Data Sets Featured Posts Generative AI Machine Learning Python Synthetic Data

How to Fix a Failing Generative Adversarial Network

In this article, I explore different front-end strategies to improve a generative adversarial network (GAN) that leads to poor synthetization, in the context of tabular data generation. It is well known that tabular data is a lot more challenging than images, when using deep neural networks for synthetization purposes. An algorithm may work very well […]

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, […]

	messerb5467 on Quantum Derivatives, GenAI, an…
	Vincent Granville on Quantum Derivatives, GenAI, an…
	Brad Messer on Quantum Derivatives, GenAI, an…
	Sanjay Gautam on Number Theory: Longest Runs of…
	Artem Melnyk on Autonomous Driving: Boosting O…