--- title: "#gradient-descent articles | Hashnode" description: "Explore 93 articles tagged #gradient-descent on Hashnode. Tutorials, guides, and how-tos from the community." url: "https://hashnode.com/tag/gradient-descent" --- Tag # #gradient-descent 93 posts · 3 followers [Siddhesh Sawant](https://hashnode.com/@siddheshsawant) · [1d ago](https://hashnode.com/posts/linear-regression/6af33e7475a9ec13b893f93b) ### [Linear Regression from First Principles: Least Squares, Geometry and Gradient Descent](https://siddheshsawant.hashnode.dev/linear-regression?utm_source=hashnode&utm_medium=feed) Fitting a straight line is the oldest trick in machine learning and still the one most worth understanding completely. This article builds linear regression from a five-point example to the normal equations, shows why least squares is a projection and a maximum-likelihood estimate at the same time, trains it with gradient descent you can steer, and then puts it to work on a real dataset to see where it holds up and where it breaks. 48 min read · [siddheshsawant.hashnode.dev](https://siddheshsawant.hashnode.dev/linear-regression?utm_source=hashnode&utm_medium=feed) [Kehinde Onasanya](https://hashnode.com/@kehindeonasanya) · [Oct 29](https://hashnode.com/posts/linear-regression-from-scratch/6ae29b0c87ba056803a12f18) ### [Linear Regression From Scratch](https://kehindeonasanya.hashnode.dev/linear-regression-from-scratch?utm_source=hashnode&utm_medium=feed) Linear regression is one of the fundamental algorithms in supervised machine learning. Its simplicity makes it an excellent starting point for understanding how models learn relationships from data, m 13 min read · [kehindeonasanya.hashnode.dev](https://kehindeonasanya.hashnode.dev/linear-regression-from-scratch?utm_source=hashnode&utm_medium=feed) [Akshay Dubey](https://hashnode.com/@devAksh) · [Oct 23](https://hashnode.com/posts/gradient-descent/6adb11689f7f3f11e7048eb5) ### [The Moment Calculus Finally Made Sense: Gradient Descent Explained Simply](https://binary-search-beyond-just-searching.hashnode.dev/gradient-descent?utm_source=hashnode&utm_medium=feed) If you've read the previous articles and made it all the way here, thank you for the patience. Cheers! 🎉 In the last stop in Algo City, we learned how an error function tells us how good or bad our l 9 min read · [binary-search-beyond-just-searching.hashnode.dev](https://binary-search-beyond-just-searching.hashnode.dev/gradient-descent?utm_source=hashnode&utm_medium=feed) [Divyajot Kaur](https://hashnode.com/@djk2004) · [Sep 26](https://hashnode.com/posts/types-of-gradient-descent/6ab72f4de2743b76a113d89f) ### [Part 23: Types of Gradient Descent: Batch, Mini-Batch & Stochastic](https://ai-beginners-journey.hashnode.dev/types-of-gradient-descent?utm_source=hashnode&utm_medium=feed) Learn how Gradient Descent processes training data in different ways and understand Batch, Mini-Batch, and Stochastic Gradient Descent with simple real-life examples. 5 min read · [ai-beginners-journey.hashnode.dev](https://ai-beginners-journey.hashnode.dev/types-of-gradient-descent?utm_source=hashnode&utm_medium=feed) [Wei Rui](https://hashnode.com/@weirui) · [Sep 24](https://hashnode.com/posts/a-software-engineer-s-intuition-for-gradient-descent-part-2-the-step-size/6ab4a7d7ceed9af2a2f2c6bf) ### [A Software Engineer's Intuition for Gradient Descent, Part 2: The Step Size](https://intuitiveml.hashnode.dev/a-software-engineer-s-intuition-for-gradient-descent-part-2-the-step-size?utm_source=hashnode&utm_medium=feed) Why one learning rate fails on ill-conditioned losses, and how per-parameter step sizes, Newton, and Adam fix it. An intuitive guide with the math. 12 min read · [intuitiveml.hashnode.dev](https://intuitiveml.hashnode.dev/a-software-engineer-s-intuition-for-gradient-descent-part-2-the-step-size?utm_source=hashnode&utm_medium=feed) [Wei Rui](https://hashnode.com/@weirui) · [Sep 24](https://hashnode.com/posts/a-software-engineer-s-intuition-for-gradient-descent/6ab4a3ad55c20975cdc8ccb8) ### [A Software Engineer's Intuition for Gradient Descent](https://intuitiveml.hashnode.dev/a-software-engineer-s-intuition-for-gradient-descent?utm_source=hashnode&utm_medium=feed) For those of us who have spent years building deterministic software systems, the machine learning realm can feel like a parallel universe. In traditional software engineering, we write the exact rule 9 min read · [intuitiveml.hashnode.dev](https://intuitiveml.hashnode.dev/a-software-engineer-s-intuition-for-gradient-descent?utm_source=hashnode&utm_medium=feed) [Divyajot Kaur](https://hashnode.com/@djk2004) · [Sep 21](https://hashnode.com/posts/gradient-descent-explained/6ab14f16fd6152efa7c6ad30) ### [Part 22: Gradient Descent: How Neural Networks Find Better Weights](https://ai-beginners-journey.hashnode.dev/gradient-descent-explained?utm_source=hashnode&utm_medium=feed) Understand how gradients, learning rate, and weight updates help models reduce error and improve their predictions. 5 min read · [ai-beginners-journey.hashnode.dev](https://ai-beginners-journey.hashnode.dev/gradient-descent-explained?utm_source=hashnode&utm_medium=feed) [Tharun](https://hashnode.com/@tharun-pandya) · [Aug 22](https://hashnode.com/posts/polynomial-regression-teaching-a-line-to-bend/6a8a080dea390438fc5306b1) ### [Polynomial Regression: Teaching a Line to Bend](https://tharunpandya.hashnode.dev/polynomial-regression-teaching-a-line-to-bend?utm_source=hashnode&utm_medium=feed) Learn polynomial regression from scratch using a delivery-time example, x^2, feature engineering, and gradient descent to fit curved patterns. 10 min read · [tharunpandya.hashnode.dev](https://tharunpandya.hashnode.dev/polynomial-regression-teaching-a-line-to-bend?utm_source=hashnode&utm_medium=feed) [Tharun](https://hashnode.com/@tharun-pandya) · [Aug 19](https://hashnode.com/posts/feature-scaling-in-machine-learning-why-it-matters/6a85536c75f2e2bc48b0eedf) ### [Feature Scaling in Machine Learning: Why It Matters](https://tharunpandya.hashnode.dev/feature-scaling-in-machine-learning-why-it-matters?utm_source=hashnode&utm_medium=feed) Learn why feature scaling matters in machine learning and how Min-Max, Mean Normalization, and Z-score scaling help gradient descent learn better. 10 min read · [tharunpandya.hashnode.dev](https://tharunpandya.hashnode.dev/feature-scaling-in-machine-learning-why-it-matters?utm_source=hashnode&utm_medium=feed) [Tharun](https://hashnode.com/@tharun-pandya) · [Aug 16](https://hashnode.com/posts/multiple-linear-regression-when-one-input-isn-t-enough/6a81e06dd14c9076a42b600f) ### [Multiple Linear Regression: When One Input Isn't Enough](https://tharunpandya.hashnode.dev/multiple-linear-regression-when-one-input-isn-t-enough?utm_source=hashnode&utm_medium=feed) Picture two different orders. Both are exactly 3 kilometers away from the restaurant. One arrives in 18 minutes. The other takes 35 minutes. If distance is the only thing our model looks at, it has no 9 min read · [tharunpandya.hashnode.dev](https://tharunpandya.hashnode.dev/multiple-linear-regression-when-one-input-isn-t-enough?utm_source=hashnode&utm_medium=feed)