How does a feedforward neural network work?

A feedforward neural network works by passing information from the input layer through one or more hidden layers to the output layer, without sending information backward through recurrent connections.

1. Input values enter the network

The network receives a set of input features:

[x1, x2, x3, ...]

These values are passed to the first hidden layer.

2. Neurons calculate weighted sums

Each neuron combines its inputs using learned weights and a bias:

z = w1x1 + w2x2 + ... + b

The weights determine how strongly each input influences that neuron.

3. Activation functions add nonlinearity

The weighted sum is passed through an activation function:

a = f(z)

Nonlinear activation functions allow the network to learn relationships that cannot be represented by a single linear transformation.

4. Information moves through hidden layers

The output of one layer becomes the input to the next:

Input → Hidden layer 1 → Hidden layer 2 → ... → Output

Each layer transforms the representation before passing it forward.

5. The output layer produces the result

The output layer is designed for the task.

For example:

• Regression can produce a continuous value.
• Binary classification can produce a probability-like score.
• Multiclass classification can produce scores or probabilities for several classes.

6. Training adjusts the weights

During training, the network's prediction is compared with the correct target using a loss function.

Backpropagation calculates gradients, and an optimizer updates the weights and biases.

Forward pass → Loss → Backpropagation → Weight update

The term feedforward describes the network architecture and forward computation. Backpropagation is still used during training to calculate how the parameters should change.

For the basic definition, see “What is a feedforward neural network?”.

In short, a feedforward neural network transforms inputs layer by layer in one forward direction, then learns better weights by minimizing prediction error during training.