Algorithm
Visualization

Interactive visualizations of classical algorithms.
Click the maze to set start & end. Play against a minimax AI.
Watch a perceptron learn a decision boundary in real time.

Dijkstra's shortest path Minimax + alpha-beta Perceptron learning
01

Dijkstra's Shortest Path

Click to place start (green) → end (red) → walls. Then run. The algorithm explores weighted cells and finds the optimal route.

Ready — click to place start/end
Start
End
Wall
Explored
Path
02

Connect Four — Minimax

Classic 4 Gewinnt. You play against an AI using minimax search with alpha-beta pruning. Minimax is an approach in decision making and game theory. The core idea is trying to find an optimal strategy under the assumption that the opponent also plays optimal. This idea can be made more powerful by adding alpha beta pruning. It is a way for the algorithm to focus on promising parts of the gametree making it over all more powerful.

Your turn — click a column

Turn

You are ■ Orange

AI is ■ Yellow

Algorithm

Minimax with alpha-beta pruning

Nodes: 0
Pruned: 0

Score

You: 0  |  AI: 0  |  Draws: 0

Game Tree — last AI move, 3 levels

AI (max) Human (min) best path cutoff
Make a move to see the game tree.
03

Perceptron Learning

The perceptron is the simplest neural network, a single neuron that learns a linear decision boundary. Click to place points of two classes, then watch it train. The boundary rotates and shifts in real time as the weights update.

Ready — click to place points
Class A
Class B
Decision boundary
Misclassified

Network

Weights

w1
0.00
w2
0.00
b
0.00

Training

Epoch: 0

Errors: 0 / 0

Accuracy: