cheatsheets ai

The collection spans fundamental mathematics (matrix operations, probability distributions, calculus for backpropagation), classical ML algorithms, deep learning frameworks (PyTorch, TensorFlow, Keras), and specialized domains including computer vision and NLP.

Each cheat sheet condenses the most frequently referenced material for its topic PyTorch tensor operations, scikit-learn estimator APIs, pandas data manipulation, matplotlib plotting patterns, activation functions and their derivatives, regularization techniques, and evaluation metrics into a format designed for fast lookup during active development.

The sheets are formatted as PDF documents optimized for printing at A4 or letter size, making them useful as desk references alongside a monitor.

The repository is open-source on GitHub and continuously updated as frameworks release new APIs and community members contribute additional reference material.

It is particularly popular among students preparing for ML interviews, researchers transitioning between frameworks, and engineers who work across multiple ML domains and need quick reference for less-frequently-used APIs.

The cheat sheet format complements more comprehensive resources like textbooks and documentation, providing the 20% of information that covers 80% of daily lookups.