# Neural Network

> A computing system inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers that process information and learn patterns.

Category: Architecture

Source: https://chipp.ai/ai/glossary/neural-network

A neural network is a computing system loosely inspired by the human brain, consisting of interconnected nodes (artificial neurons) organized in layers. Information flows through the network, with each connection having a learnable weight that determines its importance.

Neural network structure: input layer (receives data), hidden layers (process information — "deep" networks have many hidden layers), output layer (produces the result), weights (learnable parameters on connections), and activation functions (introduce non-linearity, enabling complex pattern learning).

How neural networks learn: forward pass (data flows through the network to produce output), loss calculation (comparing output to desired answer), backpropagation (calculating how each weight contributed to the error), and weight update (adjusting weights to reduce error). This cycle repeats millions of times during training.

Types of neural networks: feedforward networks (simplest — data flows one direction), convolutional networks (CNNs — specialized for images and spatial data), recurrent networks (RNNs — specialized for sequential data), transformers (the architecture behind modern LLMs), and graph neural networks (for relationship data).

Neural networks are the building blocks of modern AI. Every major AI application — from ChatGPT to self-driving cars to medical imaging — is powered by neural networks. Understanding the basic concept (layers of nodes learning patterns from data) provides useful context for working with AI systems.

## Related Terms

- [Deep Learning](https://chipp.ai/ai/glossary/deep-learning.md): A subset of machine learning using neural networks with many layers (deep networks) to learn complex patterns from large amounts of data.
- [Transformer](https://chipp.ai/ai/glossary/transformer.md): The neural network architecture that powers modern AI language models, using self-attention mechanisms to process sequences of data in parallel.
- [Machine Learning](https://chipp.ai/ai/glossary/machine-learning.md): A type of artificial intelligence where computer systems learn patterns from data to make predictions or decisions without being explicitly programmed.
- [Attention Mechanism](https://chipp.ai/ai/glossary/attention-mechanism.md): A technique in neural networks that allows the model to focus on the most relevant parts of input data when generating each part of the output.

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This term is part of the [Chipp AI Glossary](https://chipp.ai/ai/glossary), a reference of AI concepts written for builders and businesses.

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