https://sankethbk.github.io/blog/tags/gradient-descent/Recent content in Gradient-Descent on Sanketh's BlogHugo -- 0.166.0en-usMon, 07 Sep 2026 00:00:00 +0000https://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>