Notebooks
Reactive Python notebooks powered by marimo — edit code and see results update live in the browser, no install required.
Gradient Descent
Implement gradient descent variants for linear regression — full batch, stochastic, momentum, and Adagrad.
Simple Neural Network
Build a two-layer fully connected neural network from scratch using NumPy only.
Backpropagation & MLP
Build a scalar autograd engine from scratch, then implement backpropagation through cross-entropy, batch normalization, and a multi-layer perceptron — all using pure Python and NumPy.
Transformers
Build a transformer from scratch using NumPy: tokenize text, implement scaled dot-product attention, multi-head attention, and a full transformer block with residual connections and layer normalization.
ML Interview Tasks
Classic array and image manipulation problems commonly asked in ML engineering interviews — implement from scratch using NumPy.