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 doomlab.github.io
Lasso Myself Numbers - Dr. Erin Buchanan
| | www.robertkubinec.com
What's Logs Got to Do With It? - Homepage
3.3 parsecs away

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| | Ordered beta regression can give you comparable, scale-free ATEs that can still be understood in the scale of the original data-all without using logs.
| | aosmith.rbind.io
Simulate! Simulate! - Part 1: A linear model
3.7 parsecs away

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| | Where I discuss simulations, why I love them, and get started on a simulation series with a simple two-group linear model simulation.
| | josiahparry.com
Josiah Parry - Univariate Spatial Dimensionality Reduction
4.0 parsecs away

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| | www.depthfirstlearning.com
Resurrecting the Sigmoid: Theory and Practice ยท Depth First Learning
24.1 parsecs away

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| [AI summary] The provided text is a detailed exploration of the mathematical and statistical foundations of neural networks, focusing on the Jacobian matrix, its spectral properties, and the implications for dynamical isometry. The key steps and results are as follows: 1. **Jacobian and Spectral Analysis**: The Jacobian matrix $ extbf{J} $ of a neural network is decomposed into $ extbf{J} = extbf{W} extbf{D} $, where $ extbf{W} $ is the weight matrix and $ extbf{D} $ is a diagonal matrix of derivatives. The spectral properties of $ extbf{J} extbf{J}^T $ are analyzed using the $ S $-transform, which captures the behavior of the eigenvalues of the Jacobian matrix. 2. **$ S $-Transform Derivation**: The $ S $-transform of $ extbf{J} extbf{J}^T $ is...