How a Neural Network Works
An interactive simulation showing how an artificial neural network learns and how it makes decisions
Interactive Simulation
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How Does a Neural Network Work?
🔬 Neuron Structure
Each neuron receives input signals, multiplies them by weights, adds a bias, and applies an activation function to produce an output signal.
output = activation_function(Σ(inputs × weights) + bias)
🎯 Activation Function
The activation function introduces nonlinearity into the network, allowing it to learn complex patterns. The most common: ReLU, Sigmoid, Tanh.
ReLU(x) = max(0, x)
Sigmoid(x) = 1 / (1 + e^(-x))
📚 Training Process
1. Forward Pass
Input data passes through all layers of the network, producing a prediction.
2. Error Calculation
The prediction is compared with the actual value to compute the error.
3. Backpropagation
Gradients are computed and weights are updated to reduce the error.
⚡ Layer Types
Input Layer
Receives the initial data and passes it to the next layer
Hidden Layers
Process the data and detect complex patterns
Output Layer
Produces the final result or prediction