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AI From Scratch/Phase 01/Lesson 14/~90 minutes

Norms and Distances

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Your distance function defines what "similar" means. Choose wrong and everything downstream breaks.

Back to phaseMath Foundations Next lessonStatistics for Machine Learning

Phase 01

Math Foundations

01Linear Algebra Intuition 02Vectors, Matrices & Operations 03Matrix Transformations 04Calculus for Machine Learning 05Chain Rule & Automatic Differentiation 06Probability and Distributions 07Bayes' Theorem 08Optimization 09Information Theory 10Dimensionality Reduction 11Singular Value Decomposition 12Tensor Operations 13Numerical Stability 14Norms and Distances 15Statistics for Machine Learning 16Sampling Methods 17Linear Systems 18Convex Optimization 19Complex Numbers for AI 20The Fourier Transform 21Graph Theory for Machine Learning 22Stochastic Processes

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Phase 01

Math Foundations

01Linear Algebra Intuition 02Vectors, Matrices & Operations 03Matrix Transformations 04Calculus for Machine Learning 05Chain Rule & Automatic Differentiation 06Probability and Distributions 07Bayes' Theorem 08Optimization 09Information Theory 10Dimensionality Reduction 11Singular Value Decomposition 12Tensor Operations 13Numerical Stability 14Norms and Distances 15Statistics for Machine Learning 16Sampling Methods 17Linear Systems 18Convex Optimization 19Complex Numbers for AI 20The Fourier Transform 21Graph Theory for Machine Learning 22Stochastic Processes

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Resources

FAISS: A Library for Efficient Similarity SearchReading Wasserstein GAN (Arjovsky et al., 2017)Reading Locality-Sensitive Hashing (Indyk & Motwani, 1998)Reading Efficient Estimation of Word Representations (Mikolov et al., 2013)Reading sklearn.neighbors documentationReading Distance ChooserPrompt