A 100-figure field guide to charts, maps, diagrams, scientific graphics, visual diagnostics, and award-level evidence in mathematical modeling papers.

A systems-oriented foundation for deep learning, connecting generalization and optimization with accelerators, single-GPU throughput, distributed training, All-Reduce, and benchmarking.

Why database systems exist, how data moves from requirements to relations, and how keys, declarative SQL, and NULL semantics establish a reliable relational foundation.

Single-layer models built from first principles: regression, vectorization, gradient descent, feature maps, generalization, logistic regression, softmax, and cross-entropy.

A structured deep-neural-network review spanning initialization, optimization, CNNs, sequence models, attention, Transformers, and modern representation learning.

Three downloadable CS170 final-review cheat sheets collected in one place for compact, exam-focused revision.

Two downloadable CS188 final-review cheat sheets for quickly revisiting the course’s core artificial-intelligence methods.

A growing set of abstract algebra notes covering modular arithmetic, groups, homomorphisms, subgroups, symmetric groups, and the proof techniques behind them.

Notes on 2D projective geometry: homogeneous coordinates, conics, transformations, cross-ratios, points at infinity, and affine or metric rectification.

A broad machine-learning review covering Bayesian decisions, estimation, linear classifiers, neural networks, SVMs, dimensionality reduction, clustering, and ensembles.