The Living Machine Learning Reference
Search
Search
Dark mode
Light mode
Explorer
Foundations of ML
Calculus
Gradient of a function
Minimizers and minima
Linear algebra
Concepts
Collinearity
Eigenvalues and eigenvectors
Linear combination
Linear transformation
Normal to a plane
Orthogonality
Quadratic form
Row- and column-major ordering (tensor vectorization)
Vector space
Matrices
Diagonal matrix
Orthogonal matrix
Rotation matrix
Similar matrices
Singular matrix
Symmetric matrix
Unitary matrix
Operations
Batched matrix multiplication
Broadcast (algebra)
Conjugate transpose of a matrix
Determinant of a (square) matrix
Dot product of two vectors
Element-wise (Hadamard) product
Frobenius inner product
Frobenius norm
Matrix inverse
Matrix multiplication (product)
Matrix transpose
Orthogonal projection
Outer product of two vectors
Projection (projection matrix)
Pseudo-inverse of a matrix (Moore-Penrose)
Spectral norm
Matrix decompositions
Eigenvalue decomposition for a square matrix
Matrix diagonalization
Singular value decomposition (SVD)
Statistics and Information Theory
Concepts
Covariance matrix
Curse of dimensionality
Information content of a random event
Information theory notation
Moment of a function
Moment-generating functions
Describing distributions
Harmonic mean
Perplexity
Shannon entropy
Comparing distributions
Conditional entropy
Cross-entropy and Shannon entropy
Cross-entropy
Jensen-Shannon (JS) divergence (JSD)
Kullback-Leibler (KL) divergence ("relative entropy")
Mutual information
Population stability index, (Jeffreys distance, PSI)
Response of JSD and PSI to a rare event
Wasserstein metric (Earth mover's distance, EMD)
Measuring distance
Chebyshev distance (L-infinity norm)
Cosine similarity
Euclidean distance (L2 norm)
Hamming distance
Inner product (dot product) similarity
L-p norm (Minkowski distance)
Mahalanobis distance
Manhattan (taxicab) distance (L1 norm)
Additional concepts
Complex conjugate
Dirac delta "function"
Finite state machine
Heaviside step function
Kroenecker delta function
Classical ML
Neural networks
Attention
Naradaya-Watson regression
Evaluation and training
ML in production
Engineering
Tools
Practice domains
Natural language processing (NLP)
Language modeling (LM)
Home
❯
Foundations of ML
❯
Statistics and Information Theory
❯
Measuring distance
Measuring distance
8 items under this folder.
Dec 23, 2024
Manhattan (taxicab) distance (L1 norm)
Dec 23, 2024
Mahalanobis distance
Dec 23, 2024
L-p norm (Minkowski distance)
Dec 23, 2024
Inner product (dot product) similarity
Dec 23, 2024
Hamming distance
Dec 23, 2024
Euclidean distance (L2 norm)
Dec 23, 2024
Cosine similarity
Dec 23, 2024
Chebyshev distance (L-infinity norm)