Introduction to Neural Networks

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.

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The Biological Inspiration

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

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The Perceptron

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.