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🧠 Neural Network Learning

Simple neural network learning with backpropagation. Models how connection weights adjust through experience to solve classification problems like XOR or pattern recognition.

Generation: 0
Error: 1.000
Accuracy: 0%
Epochs: 0
Neural Learning:
• Forward pass: Input → Hidden → Output
• Backprop: Error signal propagates back
• Weight update: Ī”w = Ī· Ɨ error Ɨ input
• Activation: Sigmoid σ(x) = 1/(1+e⁻ˣ)