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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...
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Massively Speed-Up your Learning Algorithm, with Stochastic Thinning
You have to see it to believe it! Imagine a technique where you randomly delete as many as 80% of your observations in the training set, without decreasing the predictive power (actually improving it...
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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,...
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New Book: State of the Art in GenAI & LLMs — Creative Projects, with Solutions
With 23 top projects, 96 subprojects, and 6000 lines of Python code, this vendor-neutral coursebook is a goldmine for any analytic professional or AI/ML engineer interested in developing superior GenAI...
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Synthesizing Multi-Table Databases: Model Evaluation & Vendor Comparison
Synthesizing multi-table tabular data presents its own challenges, compared to single-table. When the database contains date columns such as transaction or admission date, a frequent occurrence in...
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A New Type of Non-Standard High Performance DNN with Remarkable Stability
I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI. The original...
View Article10 Tips to Boost Performance of your AI Models
These model enhancements techniques apply to deep neural networks (DNNs) used in AI. The focus is on the core engine that powers all DNNs: gradient descent, layering and loss function....
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Watermarking and Forensics for AI Models, Data, and Deep Neural Networks
In my previous paper posted here, I explained how I built a new class of non-standard deep neural networks, with various case studies based on synthetic data and open-source code, covering problems...
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New Book: No-Blackbox, Secure, Efficient AI and LLM Solutions
Large language models and modern AI is often presented as technology that needs deep neural networks (DNNs) with billions of Blackbox parameters, expensive and time consuming training, along with GPU...
View Article30 Articles Shaping the Future of Enterprise AI in 2026
Over several decades, I unlearned everything that I learned in college classes, and built a new discipline from scratch, as much different from traditional AI than it is from standard machine learning,...
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