All linear-algebra probability deep-learning measure-theory information-theory supervised-learning generative-modeling optimization statistics eigenvalues eigenbasis diagonalization symmetric-matrices dynamical-systems quadratic-forms positive-definiteness hessian gradients calculus lagrangian constrained-optimization svd matrix-factorization pca covariance variance dimensionality-reduction rank subspaces projections vectors geometry random-variables expectation distributions gaussian kl-divergence densities maximum-likelihood cross-entropy rayleigh-quotient sigma-algebra duality span basis law-of-large-numbers central-limit-theorem degrees-of-freedom confidence-intervals t-distribution concentration-inequalities backpropagation automatic-differentiation jacobian weight-initialization reparameterization-trick

@misc{saichandran2026notes,
  author       = {Saichandran, Ketan Suhaas},
  title        = {Deep Learning Notes: Mathematics \& Theory},
  howpublished = {\url{https://ketansuhaas.github.io/notes/}},
  year         = {2026},
  note         = {Accessed: YYYY-MM-DD}
}