What is deep learning?
Deep learning is a sub-field of machine learning that uses neural networks with many layers.
What is it?
Deep learning is the part of machine learning that uses neural networks with many layers. Each layer picks out slightly more complex patterns from the data. This approach is behind most of today’s progress with language, images and speech.
An everyday example
When your phone sorts photos by faces or by “beach” and “food”, a deep learning model is usually doing it.
Why does it matter?
Deep learning made translators, speech recognition and language models possible, but it needs lots of data and computing power and is hard to explain.
A common misconception
That deep learning is all of AI, or that “neural” means it works the way the brain does.
In more detail
Why “deep”?
Because the model uses many layers, one after another, to transform information.
What happens in the layers?
Each layer changes the numerical representation of the data. During training, the connections between layers are adjusted.
Is deep learning all of AI?
No. There are also symbolic, statistical, search-based and other approaches.
What are its weaknesses?
It can need a lot of data and computing power. It is harder to explain individual predictions and to make sure it works beyond the examples it learned from.
Remember: “Neural” does not mean “biologically the same as the brain”.
Reliable sources
Last reviewed 1 November 2026. An explainer, not professional, legal or medical advice. For rules and figures that change, check the official version.