Numpy

Training a 2-Layer Network in NumPy: From Scalar to Vectorized Backprop The previous note did the backward pass for a small 2-layer network by hand, one scalar at a time. That is the best way to understand what backprop actually does. This note takes the next step: turn that scalar walk into compact, vectorized NumPy code. The math is unchanged; the only thing that changes is notation. I will introduce every matrix slowly — what its rows and columns mean, where the division by the batch size comes from, and why it is exactly the same algorithm you already did by hand. ...

ML Refresher: Linear and Logistic Regression This is the first note in the ML → Deep Learning → Transformers → LLMs series. The goal is to rebuild working memory of the basics before we get to neural networks: what a model is, how a loss function measures error, and how gradient descent tunes parameters. We will implement linear regression and logistic regression from scratch in NumPy, then compare with scikit-learn. If the code and gradients feel obvious, you are ready for the next note (computational graphs and backprop). If not, this is exactly the foundation to lock down first. ...