What is Neural Network? Definition & Guide

What is Neural Network?

neural network is a computational model composed of interconnected layers of mathematical units called neurons. Each connection has a weight that is adjusted during training via backpropagation. Neural networks learn hierarchical patterns: early layers detect simple features, deeper layers detect complex ones. Convolutional neural networks power computer vision; transformer neural networks power large language models.

A neuron in an artificial neural network is a simple mathematical function: it takes several numerical inputs, multiplies each by a weight, sums them up, and passes the result through a non-linear activation function (like ReLU or sigmoid) to produce an output. Individual neurons are simple; the power comes from connecting millions or billions of them into deep networks where each layer's output becomes the next layer's input. The network learns by adjusting weights through backpropagation until its predictions on training data match the desired outputs. The resulting learned weight configuration — the model — captures the patterns that allow generalisation to new inputs.

From an enterprise deployment perspective, understanding neural networks at the conceptual level matters for three practical decisions. First, choosing the right architecture for the task: transformer networks for language, CNNs for images, graph neural networks for relational data. Second, understanding hardware requirements: larger networks require more GPU memory and compute, which determines what hardware you need for on-premise deployment. Third, understanding robustness: neural networks are pattern matchers, which means they can fail unexpectedly on inputs that differ significantly from their training distribution — a consideration for production reliability and guardrail design.

Also known as: Artificial Neural Network, ANN

Key Points

Core idea
Early layers detect simple features (edges in images, word-level patterns in text), while deeper layers learn complex, abstract representations (objects, semantic meaning, conceptual relationships).
Why it matters
CNNs for spatial data (images), transformers for sequential data (text, audio), graph neural networks for relational data. Architecture choice is the most important design decision for a new AI application.
Enterprise use
Larger neural networks are more capable but require more compute and memory. Enterprise deployment decisions require balancing capability requirements against available hardware.

How It Works

How Neural Network works

Define the purpose, inputs, and success criteria that Neural Network must support.
Apply Neural Network in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.

How Fluid AI Uses This

Neural networks running on customer hardware.

Fluid AI deploys neural network-based AI models entirely on customer infrastructure, with no dependency on external compute providers. Model weights stay inside your data center.

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Topics Covered

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Neural Network