A neural network is not magic.

It is a system that transforms input into output through layers.

The real question is:

How does that system learn from mistakes?

Core Idea

A neural network takes data, passes it through connected layers, and produces an output.

During training, it adjusts internal values so future outputs become better.

Those internal values are mainly weights and biases.

So the simplest view is:

Input → Layers → Output → Error → Update

That loop is the foundation of neural network learning.

The Key Structure

A basic neural network looks like this:

Input Layer → Hidden Layers → Output Layer

Each layer transforms the data.

A neuron usually computes:

z = w · x + b

Then an activation function transforms it:

a = activation(z)

Where:

x = input

w = weight

b = bias

z = raw score

a = activated output

This is the basic building block.

Implementation View

At a high level, training works like this:

take input data

run forward propagation

compute prediction

compare prediction…