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asthasr.github.io | ||
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www.adamconrad.dev
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| | | | | Follow along with Steven Skiena's Fall 2018 algorithm course applied to the JavaScript language. | |
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hurryabit.github.io
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| | | | | I demonstrate how to (ab)use generators to transform any recursive function into an iterative one with nearly zero code changes. | |
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aneesh.mataroa.blog
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www.depthfirstlearning.com
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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... | ||
