Learn how to teach or explain greedy and backtracking algorithms using examples, analogies, and code snippets. These techniques can solve optimization and search problems.

Learn how to use simple language, visual aids, storytelling, math simplification, and context to explain your ML algorithm results to a non-technical audience.

This repository contains educational materials to help you understand the DBScan (Density-Based Spatial Clustering of Applications with Noise) algorithm. The aim is to provide both theoretical ...

For example, certain parts of a neural network can be shown to activate in response to specific components of an input image—edges, different types of texture, or faces, for example.

The algorithms often rely on variants of steepest descent for their optimizers, for example stochastic gradient descent (SGD), which is essentially steepest descent performed multiple times from ...