https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749
Posts Tagged ‘aimath0mg’
Why Machines Learn: The Elegant Math Behind Modern AI: Ananthaswamy, Anil: 9780593185742: Amazon.com: Books
July 30, 2025iPhone Notebook export for Why Machines Learn: The Elegant Math Behind Modern AI
July 30, 2025Your Notebook exported from Why Machines Learn: The Elegant Math Behind Modern AI is
https://www.goodreads.com/notes/195887568-why-machines-learn/114528832-mark-gerstein?ref=rsp
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
Reconciling modern machine-learning practice and the classical bias–variance trade-off | PNAS
May 19, 2025“double descent”
Demystifying the XOR problem – DEV Community
May 18, 2025Frontotemporal dementia – Wikipedia
May 18, 2025https://en.wikipedia.org/wiki/Frontotemporal_dementia
Pick’s disease
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