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nhigham.com | ||
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fa.bianp.net
There's a fascinating link between minimization of quadratic functions and polynomials. A link that goes deep and allows to phrase optimization problems in the language of polynomials and vice versa. Using this connection, we can tap into centuries of research in the theory of polynomials and shed new light on ... |
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djalil.chafai.net
This post is devoted to few convex and compact sets of matrices that I like. The set \( {\mathcal{C}_n} \) of correlation matrices. A real \( {n\times n} \) matrix \( {C} \) is a correlation matrix when \( {C} \) is symmetric, semidefinite positive, with unit diagonal. This means that \[ C_{ii}=1, \quad C_{ji}=C_{ji},\quad \left\geq0 \] for every \(... |
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quomodocumque.wordpress.com
I met Mike Freedman last week at CMSA and I learned a great metaphor about an old favorite subject of mine, random walks on groups. The Heisenberg group is the group of upper triangular matrices with 1's on the diagonal: You can take a walk on the integral or Z/pZ points of the Heisenberg group... |
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www.paepper.com
[AI summary] This article explains how to train a simple neural network using Numpy in Python without relying on frameworks like TensorFlow or PyTorch, focusing on the implementation of ReLU activation, weight initialization, and gradient descent for optimization. |
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