Interactive simulation of how weights and biases affect a neuron's decision to 'fire' or pass a signal.
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The Brain of AI: Neural Networks Explained
Weights and Biases
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Inside the network, connections have strengths called 'weights'. Think of weights as volume knobs. If a weight is high, that input is very important. If it's low, the input matters less. There is also a 'bias', which acts like a threshold. Even if inputs are weak, a high bias might push the neuron to fire anyway. Together, weights and biases allow the network to tune itself to recognize specific patterns accurately.
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