The AI Neurons
ML Cheatsheets
Quick reference guides for essential machine learning concepts.
Linear Classifiers
Quick reference for decision boundaries, hyperplanes, and classification fundamentals.
Perceptron
Error-based learning, update rules, and convergence theorem at a glance.
Support Vector Machines
Margin maximization, hinge loss, and the kernel trick condensed.
Feature Representation
Encoding techniques, polynomial features, and modern embeddings.
Regression
Linear regression, MSE, gradient descent, and practical tips.
Gradient Descent
Optimization fundamentals, learning rates, and modern adaptive methods.