https://sankethbk.github.io/blog/tags/logistic-regression/Recent content in Logistic-Regression on Sanketh's BlogHugo -- 0.166.0en-usSat, 12 Sep 2026 00:00:00 +0000https://sankethbk.github.io/blog/posts/ml/2026-09-12-2-single-neuron-as-a-graph/Sat, 12 Sep 2026 00:00:00 +0000https://sankethbk.github.io/blog/posts/ml/2026-09-12-2-single-neuron-as-a-graph/<h1 id="computational-graphs-part-3-a-single-neuron-and-logistic-regression">Computational Graphs, Part 3: A Single Neuron and Logistic Regression</h1> <p>The <a href="https://sankethbk.github.io/blog/posts/ml/2026-09-12-1-branching-why-gradients-add">previous note</a> showed how gradients add when one input feeds multiple operations. With that in place, we can now look at a real model: a single neuron. We will draw it as a graph, run the forward pass and backward pass by hand, and then connect it back to the logistic regression from the <a href="https://sankethbk.github.io/blog/posts/ml/2026-09-07-ml-refresher-linear-logistic-regression">first note</a>.</p> <hr> <h2 id="1-what-you-will-learn">1. What you will learn</h2> <ul> <li>How a single neuron is a small computational graph.</li> <li>The forward pass through a weighted sum and an activation function.</li> <li>The backward pass through the same graph.</li> <li>Why logistic regression is exactly a one-neuron network with sigmoid activation.</li> <li>How the cross-entropy loss fits into the graph as an extra node.</li> <li>Why the gradient formula from logistic regression matches the chain-rule result.</li> </ul> <hr> <h2 id="2-a-single-neuron">2. A single neuron</h2> <p>A neuron with two inputs has three steps:</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>