What is Deep Learning?

Machine learning with neural networks of many layers, each turning data into more abstract features.

Basic

Deep learning is a kind of machine learning that uses neural networks with many layers. "Deep" refers to those layers. Each one turns its input into something a little more abstract: in an image, early layers might pick up edges, later ones shapes, and the last ones whole objects.

Nobody programs these features by hand. The network finds them itself during training, by adjusting its parameters step by step to reduce its errors.

Almost everything people call AI today is deep learning: large language models, image generators, speech recognition and translation. In their 2015 review, Yann LeCun, Yoshua Bengio and Geoffrey Hinton listed the fields it had already changed, from speech and object recognition to drug discovery and genomics.

Why it matters for you

Deep learning learns from examples, not from rules. That makes it powerful and flexible, but also dependent on its data and hard to explain. Why a model gave a particular answer is often unclear, which is the field of explainability.

Own it

When a vendor says "AI", ask what kind: fixed rules, classic machine learning or deep learning. They differ in cost, in how much data they need and in how well you can explain their decisions.

Where it comes from

The standard reference is the review Deep learning by Yann LeCun, Yoshua Bengio and Geoffrey Hinton, published in Nature in May 2015. The underlying training method, backpropagation, goes back to the 1980s (see training).

Machine Learning Neural Network Training Transformer Large Language Model (LLM)Explainability

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