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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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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Math-free, Parameter-free Gradient Descent in Python
Entitled “Math-free, Parameter-free Gradient Descent in Python”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. I discuss techniques related to the gradient descent method in 2D. The goal is to find the minima of a target function, called the cost function. The values of the function are computed at […]
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New Perspective on the Riemann Hypothesis
Entitled “New Perspective on the Riemann Hypothesis”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. In about 10 pages (plus Python code, exercises and figures), this article constitutes a scratch course on the subject. It covers a large range of topics, both recent as well as unpublished, in a […]
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Detecting Subtle Departures from Randomness
Entitled “Detecting Subtle Departures from Randomness”, the full version in PDF format is accessible in the “Free Books and Articles” section, here. Also discussed in details in chapter 9 in my book “Intuitive Machine Learning and Explainable AI”, available here. Figure 1 below shows two plots arising from two different, non-periodic pseudo-random sequences of +1 and -1 in […]
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