AI Algorithms Explained To Kids by Denis

Algorithm

Purpose

Logistic Regression

Predicts yes/no outcomes.

Recurrent Neural Networks (RNN)

Understands sequences like stories.

K-Means Clustering

Groups similar items together.

Principal Component Analysis (PCA)

Packs important data into a small space.

Autoencoders

Compresses and reconstructs images.

Neural Networks

Learns from examples like our brain cells

Reinforcement Learning

Learns with rewards, like training a dog

Q-Learning

Finds the best path in a maze

Naive Bayes

Predicts outcomes based on prior knowledge.

k-Nearest Neighbors (k-NN)

Finds similar items by asking friends

Bayesian Networks

Predicts by considering different factors.

Support Vector Machine (SVM)

Separates items with the straightest line

Genetic Algorithms

Mixes traits to create the best solution

Linear Regression

Predicts outcomes based on past data

Random Forests

Combines multiple answers for accuracy

Convolutional Neural Networks (CNN)

Recognizes patterns like faces

Decision Trees

Makes decisions with yes/no questions.

Gradient Boosting

Improves with each small mistake