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Neural networks are machine learning models inspired by the structure of the biological brain. They consist of layers of interconnected neurons (nodes) that learn to transform inputs into outputs by adjusting their internal weights during training. Neural networks are the foundation of deep learning and power many modern AI applications — from image recognition and language translation to game playing and drug discovery.
A biological neuron receives signals through dendrites, processes them in the cell body, and transmits the output through the axon to other neurons. An artificial neuron mimics this:
| Biological | Artificial |
|---|---|
| Dendrites (inputs) | Input features (x1, x2, ...) |
| Synaptic weights | Learnable weights (w1, w2, ...) |
| Cell body (processing) | Weighted sum + activation function |
| Axon (output) | Output value |
The perceptron is the simplest neural network — a single neuron that computes a weighted sum of inputs and applies a step function:
output = step(w1x1 + w2x2 + ... + wn*xn + b)
Where the step function returns 1 if the sum is positive, 0 otherwise.
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