these slides might be related to the event in this link:
What is the relation between Logistic Regression and Neural Networks and when to use which?
June 29, 2025What is the relation between Logistic Regression and Neural Networks and when to use which?
https://sebastianraschka.com/faq/docs/logisticregr-neuralnet.html
Universal approximation theorem – Wikipedia
June 29, 2025QT:{{”
In the mathematical theory of artificial neural networks, universal approximation theorems are theorems[1][2] of the following form: Given a family of neural networks, for each function f
{\displaystyle f} from a certain function space, there exists a sequence of neural networks….That is, the family of neural networks is dense in the function space.
The most popular version states that feedforward networks with non-polynomial activation functions are dense in the space of continuous functions between two Euclidean spaces, with respect to the compact convergence topology.
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https://en.wikipedia.org/wiki/Universal_approximation_theorem
A Scenic Tour of Red Tape: Tracking the Slowest High-Speed Train in the Country – The New York Times
June 29, 2025Branch, J. (2025, May 4). A scenic tour of red tape: tracking the slowest High-Speed train in the country. The New York Times. https://www.nytimes.com/2025/05/04/us/high-speed-rail-california.html
