https://sankethbk.github.io/blog/tags/numpy/Recent content in Numpy on Sanketh's BlogHugo -- 0.166.0en-usMon, 14 Sep 2026 00:00:00 +0000https://sankethbk.github.io/blog/posts/ml/2026-09-14-1-training-a-2-layer-network-in-numpy/Mon, 14 Sep 2026 00:00:00 +0000https://sankethbk.github.io/blog/posts/ml/2026-09-14-1-training-a-2-layer-network-in-numpy/<h1 id="training-a-2-layer-network-in-numpy-from-scalar-to-vectorized-backprop">Training a 2-Layer Network in NumPy: From Scalar to Vectorized Backprop</h1> <p>The <a href="https://sankethbk.github.io/blog/posts/ml/2026-09-12-3-backpropagation-in-a-fully-connected-network">previous note</a> 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.</p> <p>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.</p>https://sankethbk.github.io/blog/posts/ml/2026-09-07-ml-refresher-linear-logistic-regression/Mon, 07 Sep 2026 00:00:00 +0000https://sankethbk.github.io/blog/posts/ml/2026-09-07-ml-refresher-linear-logistic-regression/<h1 id="ml-refresher-linear-and-logistic-regression">ML Refresher: Linear and Logistic Regression</h1> <p>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.</p> <p>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.</p>