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Machine Learning

Neural Network

A machine learning model loosely inspired by the brain, built from layers of interconnected nodes that learn patterns.

By Sitebard TeamUpdated February 14, 2026

In plain English

A neural network is a computer model inspired by how brain cells connect. It learns by adjusting many tiny connections until it can recognize patterns, like telling cats from dogs in photos.

Technical definition

A neural network is a parameterized model composed of layers of interconnected nodes that apply weighted sums and non-linear activation functions to inputs. It is trained via gradient descent and backpropagation to minimize a loss function, and deep networks stack many such layers to learn hierarchical representations.

Business use case

Businesses rely on neural networks to power image recognition, speech-to-text, fraud detection, and recommendation engines. Their ability to learn complex, non-linear patterns makes them effective on data that simpler models struggle with.

Example

A photo app uses a neural network to automatically group pictures by the people in them, recognizing the same face across different lighting and angles.

Frequently asked questions

A neural network is a machine learning model made of layers of connected nodes, or 'neurons,' that adjust their connections during training to recognize patterns in data.

Deep learning is the use of neural networks with many layers, which allows them to learn complex patterns in large datasets such as images, audio, and text.

It learns by adjusting the weights of its connections through a process called backpropagation, which gradually reduces the difference between its predictions and the correct answers.

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