Problem library
ML building blocks, one implementation at a time.
Practice the functions, model classes, and training loops inside production ML systems.
KNN Classifier
Fit a complete nearest-neighbor classifier and predict labels for new examples.
K-Means Clustering
Train a deterministic K-Means model from initial centroids through convergence.
Gaussian Mixture Model
Fit a diagonal-covariance Gaussian mixture model with expectation-maximization.
Principal Component Analysis
Learn deterministic principal directions and use them for dimensionality reduction and reconstruction.
Linear Regression Trainer
Train linear regression end to end with explicit gradients and parameter updates.
Logistic Regression Trainer
Train a numerically stable binary classifier with gradient descent.
Two-Layer Neural Network Trainer
Implement forward propagation, manual backpropagation, and prediction for a small MLP.