Review Deep Learning

Deep learning is a form of machine learning that uses neural networks with many processing layers to learn complex patterns from data.

A deep-learning system transforms an input through a sequence of learned layers. Different layers can become useful for different features, allowing the model to handle complex text, images, audio, and other data.

Deep learning supports many modern generative and recognition systems, but it can require substantial data, computing power, evaluation, and operational controls.

A deep-learning model learns visual features across many layers to identify objects in photographs.

Deep learning is inspired loosely by ideas about biological neurons, but it is not a digital replica of a human brain and does not establish humanlike understanding.

Many learned layers turn raw input into useful patterns.

* [[machine-learning|Machine Learning]] * [[neural-network|Neural Network]] * [[compute|Compute]] * [[training-data|Training Data]]

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

* Continue with [[neural-network|Neural Network]] and [[compute|Compute]].