11 docs tagged with "machine-learning"

A foundation map for convex sets, convex functions, duality, and optimization problems with global guarantees.

A path through MLPs, modern activations, RNNs, attention, and Transformers that also treats leakage and distribution shift as part of model evaluation.

A foundation map for measuring uncertainty, information, compression limits, and distributional difference.

A foundation map for vectors, linear transformations, matrix factorization, and data-oriented applications.

Gaussian generative classifiers whose shared or class-specific covariance assumptions produce linear or quadratic decision boundaries.

A selection map for linear predictors, regularization, classification links, and feature transformations.

The linear prediction model, squared-error objective, solution methods, and the assumptions that determine what its coefficients mean.

Why logarithmic loss measures probabilistic classification error and how calculus connects it to likelihood optimization.

A map from problem formulation and evaluation to supervised, unsupervised, sequential, and deep learning methods.

A compact vocabulary for models, losses, empirical risk, likelihood, regularization, optimization, and generalization.

Multiclass linear classification with logits, softmax probabilities, cross-entropy, and clear boundaries around multilabel tasks.