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 […]
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Massively Speed-Up your Learning Algorithm, with Stochastic Thinning
Dramatically Speed-Up your Learning Algorithm, with Stochastic Thinning. Includes use case, Python code, regression and neural network illustrations.
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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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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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Fast Classification and Clustering via Image Convolution Filters
Subtitled “Alternative to Generative Mixture Models”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. It is also described in details in my book “Stochastic Processes and Simulations: A Machine Learning Perspective”, available here. I explain, with Python code and numerous illustrations, how to turn traditional tabular data into images, […]
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New Book: Efficient Deep Learning
Subtitled “Fast, smaller, and better models”. This book goes through algorithms and techniques used by researchers and engineers at Google Research, Facebook AI Research (FAIR), and other eminent AI labs to train and deploy their models on devices ranging from large server-side machines to tiny microcontrollers. The book presents a balance of fundamentals as well […]
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Amazing Neural Network Video Demonstration
I recently posted an article featuring a very deep neural network in action (250 layers), see here. Each frame in the video represented one layer, with the signal propagating from one layer to the next. In the last layer, the whole space was classified, in the sense that any new observation was immediately assigned to […]
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New Neural Network with 500 Billion Parameters
Google just published a research article about its Pathways Language Model (PaML), a neural network with 500 billion parameters. It is unclear to me how many layers and how many neurons (also called nodes) it can handle. A parameter in this context is a weight attached to a link between two connected neurons. So the […]
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Very Deep Neural Networks Explained in 40 Seconds
Very deep neural networks (VDNN) illustrated with data animation: a 40 second video, featuring supervised learning, layers, neurons, fuzzy classification, and convolution filters. Discussed in details with Python code in my book “Intuitive Machine Learning and Explainable AI”, available here. It is said that a picture is worth a thousand words. Here instead, I use […]
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