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When Gradient Descent Is a Kernel Method
| | www.ethanepperly.com
Five Interpretations of Kernel Quadrature Ethan N. Epperly
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| | francisbach.com
Unraveling spectral properties of kernel matrices - I - Machine Learning Research Blog
2.6 parsecs away

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| | [AI summary] The blog post discusses the spectral properties of kernel matrices, focusing on the analysis of eigenvalues and their estimation using tools like the matrix Bernstein inequality. It also covers the estimation of the number of integer vectors with a given L1 norm and the relationship between these counts and combinatorial structures. The post includes a detailed derivation of bounds for the difference between true and estimated eigenvalues, highlighting the role of the degrees of freedom and the impact of regularization in kernel methods. Additionally, it touches on the importance of spectral analysis in machine learning and its applications in various domains.
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
2.6 parsecs away

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| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | blog.scottlogic.com
The basics of the Layered Neural Network
8.3 parsecs away

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| Recently I've been learning about Neural Networks and how they work. In this blog post I write a simple introduction in to some of the core concepts of a basic layered neural network.