Review Neural Network

A neural network is a model made of connected layers of numerical operations whose adjustable weights are learned from data.

Information passes through connected units that transform it. Training adjusts the strength of those connections so the network becomes better at a defined task.

Neural networks are a core building block of deep learning and many current AI systems. Their performance depends on architecture, data, training choices, and evaluation—not the name alone.

A neural network receives measurements from a machine and estimates whether the equipment is operating normally or showing signs of failure.

The word neural does not mean the system contains biological neurons or thinks like a human brain. It is a mathematical model with limited biological inspiration.

Connected numerical layers learn how strongly signals should matter.

* [[deep-learning|Deep Learning]] * [[weights|Weights]] * [[parameters|Parameters]] * [[machine-learning|Machine Learning]]

* [Google for Developers, Machine Learning Glossary](https://developers.google.com/machine-learning/glossary) — technical definition and examples of neural networks. * [NIST AI 100-2 E2025, Adversarial Machine Learning](https://doi.org/10.6028/NIST.AI.100-2e2025) — NIST terminology for neural-network models.

* Follow the learned values through [[weights|Weights]] and [[parameters|Parameters]].