Welcome to an interactive journey into the inner workings of artificial neural networks. This page provides high-level visualizations that show how neural networks and convolutional neural networks (CNNs) learn and process information.
Each visualization presents a key concept in machine learning, from how individual neurons process inputs to how entire networks learn through backpropagation. Interact with the controls below to see neural networks learn in real-time and gain intuition for these powerful AI systems.
Neural Networks
Neural Network Learning to Predict Digit "3"
A simple neural network with 4 inputs, 2 hidden layers (3 nodes each), and 10 outputs (digits 0-9)
Note: Network starts heavily biased toward digit "7" to demonstrate gradual learning
1. Input Features
Four simple features representing the number "3" (simplified for visualization)
2. Inside a Node
Watch how one hidden node processes inputs with weights and bias
3. Initial Prediction
The network starts with random weights and makes wrong predictions
4. Backpropagation
Error signals flow backward to adjust weights and reduce mistakes
5. Updated Weights
The same node with adjusted weights after learning from the error
6. Better Prediction
After many iterations, the network learns to make correct predictions
Convolutional Neural Networks
Interactive CNN visualization with convolution, pooling, and feature map animations.