# Neural Network

> A neural network is a computational model loosely inspired by biological neurons, in which weighted connections between simple units learn to map inputs to outputs.

Category: Foundational
Source: https://impetora.com/glossary/neural-network
Part of: Impetora AI consulting glossary (https://impetora.com/glossary)

## What is Neural Network?

A neural network is built from layers of nodes. Each connection has a weight that is updated during training using gradient descent and back-propagation. Architectures include feed-forward networks, convolutional networks for images, recurrent networks for sequences, and transformer networks for language. The term is broad and now usually refers to deep architectures with many layers.

## How does Neural Network apply to enterprise AI?

Most enterprise AI components shipped in 2026 are neural networks under the hood, even when wrapped in a higher-level API. Buyers do not configure them directly, but model choice, size, and provider have direct cost, latency, and compliance implications.

## Related terms

- [Deep Learning](https://impetora.com/glossary/deep-learning) - Deep Learning is a branch of machine learning that uses multi-layer neural networks to learn hierarchical representations from raw data.
- [Foundation Model](https://impetora.com/glossary/foundation-model) - A foundation model is a large neural network pre-trained on broad data and designed to be adapted to many downstream tasks.
- [Inference](https://impetora.com/glossary/inference) - Inference is the act of running a trained model on new inputs to produce predictions or generated output.
- [Embedding](https://impetora.com/glossary/embedding) - An embedding is a dense numerical vector that represents a piece of content (text, image, audio) in a way that semantically similar items end up close together in the vector space.

## External references

- [DeepLearning.AI primer](https://www.deeplearning.ai/resources/)

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