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What Is a Neural Network? Weights, Biases, and Activation Functions

An Artificial Neural Network (ANN) is a computational model inspired by the biological neural circuits of the human brain, composed of interconnected processing units (neurons) that pass signals through weighted connections.

💡 Plain-English Analogy

Think of an artificial neuron like a committee member voting on a decision. Each member weighs different pieces of evidence (weights). If the combined evidence is strong enough to cross a certain threshold (bias and activation function), the member raises their hand (fires an output signal) to the next committee.

⚙️ Architecture & Under the Hood

Mathematically, an artificial neuron computes the dot product of its input vector x and weight vector w, adds a scalar bias b, and applies an activation function: y = f(w · x + b). Backpropagation applies the calculus chain rule backwards from the loss function to compute partial derivatives with respect to each weight.

Anatomy of a Single Artificial Neuron

Every node calculates a weighted sum of its inputs before applying a non-linear threshold.

Input x1 ──(weight w1)──┐
Input x2 ──(weight w2)──┼──▶ [ Sum: Σ (wi * xi) + b ] ──▶ [ Activation f() ] ──▶ Output y
Input x3 ──(weight w3)──┘              ▲
                                (Bias b)

Frequently Asked Questions

What is backpropagation?

Backpropagation is the algorithmic process of computing the gradient of the loss function with respect to every weight in the network via the chain rule of calculus, allowing gradient descent to adjust weights in the direction that minimizes errors.