Long-form notes and reference material, organized by subject.
English
Algorithms and Data Structures:
definitions of common algorithms (sorting, searching, graph) and data structures (lists, trees, hashes) with code examples.
Numerical Methods:
algorithms for root finding, integration, differential equations, with step-by-step implementations.
Statistics Notes:
explanations of probability distributions, hypothesis tests, regression techniques, illustrated by sample datasets.
Stanford Machine Learning:
organized lecture notes covering linear/logistic regression, neural networks, support vector machines, with sample code.
Numpy Tutorials:
examples of array creation, indexing, broadcasting, linear algebra routines and performance tips.