Alexandria Library

A curated collection of awesome articles

Here are some ressources related to Machine learning, Classification, Regularization. Hoping you will find learning material, alternative tools or just some interesting stuff.

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Articles

K-Nearest Neighbors

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Gaussian Processes from Scratch (2019)

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Gradient Descent Models Are Kernel Machines

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Every Model Learned by Gradient Descent Is Approximately a Kernel Machine

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Linear Regression

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An Idiot’s guide to Support vector machines (2003) [pdf]

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A visual explanation for regularization of linear models

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Introduction to Support Vector Machines in Machine Learning

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Using JAX, numpy, and optimization techniques to improve separable image filters

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A Visual Exploration of Gaussian Processes

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Principal component analysis: pictures, code and proofs (2018)

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Nonnegative matrix factorization

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Gaussian Processes Are Not So Fancy

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An Idiot’s guide to Support vector machines (2003) [pdf]

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Gaussian Processes for Machine Learning (2010)

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A New Angle on L2 Regularization

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Executing gradient descent on the earth

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Linear Regression A Tale of a Transform

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The often-overlooked random forest kernel

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ML Basics: K Nearest Neighbors in Ruby

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An Introduction to Support Vector Machines

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Linear regression

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Machine Learning 101: What Is Regularization?

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Have You Tried Using a Nearest Neighbor Search?

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Machine Learning: Neural Network vs Support Vector Machine

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Every model learned by gradient descent is approximately a kernel machine (2020)

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