# machine learning
The machine-learning tag collects simulations that show algorithms working, one iteration at a time, instead of hiding the process behind a black box. Gradient Descent Visualiser lets you watch a point roll downhill toward a minimum, which underlies most model training, while Linear Regression — OLS Least Squares fits a line to scattered data in real time. Two separate K-Means Clustering simulations show unlabeled points sorting themselves into groups, Perceptron builds up the simplest possible neural network from a single decision boundary, Random Forest Classifier shows many decision trees voting together, and t-SNE Visualiser demonstrates how high-dimensional data gets squashed down into a 2D map you can actually look at.
Found: 7 simulations · 0 articles
🧪 Simulations (7)
Simulations in this topic include Gradient Descent Visualiser, K-Means Clustering — Machine Learning Visualiser, K-Means Clustering — Interactive Machine Learning, Linear Regression — OLS Least Squares, Perceptron and Random Forest Classifier.
🤖 Facts about this collection
- 7 simulations are tagged machine-learning, covering both classic statistical methods and neural-network basics.
- Two separate simulations — K-Means Clustering — Machine Learning Visualiser and K-Means Clustering — Interactive Machine Learning — cover the same unsupervised clustering algorithm from different builds.
- Perceptron is the only entry that models an actual (single-layer) neural network learning a decision boundary.
- Random Forest Classifier and Linear Regression — OLS Least Squares represent the ensemble and regression sides of supervised learning, respectively.
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