Basic Feedforward Neural Network Diagram

Basic Feedforward Neural Network

A feedforward neural network moves input features through one or more hidden layers to produce an output prediction. Each hidden layer learns a new representation of the data, using the weights and activation functions learned during training. This diagram gives a readable first view of that flow: features enter on the left, transformations occur in the hidden layers, and a prediction leaves on the right. It is suitable for a lesson, an architecture overview, or a model card introduction. It is not a detailed neuron-level drawing, so it does not show individual weights, bias terms, or activations. Add those only when the audience needs to inspect the mathematical model.

Type Control-systems block diagramStandard Ogata / standard controls textbook conventionEngine schematex-blockdiagramUpdated 9/24/2026

The request

“Create a neural network diagram with input features, two hidden layers, and one output prediction.”

Then tryAdd a third hidden layer.Rename the output prediction to customer churn probability.

What is in this drawing

Read the decisions behind it.

01Inputs

Name the features entering the model.

02Hidden layers

Show the learned transformation stages.

03Output

Identify the specific prediction returned.

Use this simple form when teaching the basic direction of information flow in a feedforward neural network.

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FAQ

Common questions

What is a hidden layer?

It is a layer between the inputs and outputs that learns intermediate patterns from the training data.

Why is it called feedforward?

Information moves from input to output without a feedback connection to an earlier layer.

What is an output prediction?

It is the model result, such as a class, probability, or numeric estimate.