Three patterns where deep learning routinely wins
Perception — turning raw pixels, waveforms, or text into structured predictions. Image classification, object detection, automatic speech recognition, OCR, named-entity recognition. Decades of hand-crafted feature pipelines were replaced in a few years.
Generation — producing structured output that follows the data distribution. Translation, image synthesis, code completion, text-to-speech, music generation. The model learns the manifold of the data and samples from it.
Sequence reasoning under context — taking a long input and producing an output that depends on global structure. Document understanding, code refactoring across many files, multi-turn dialog, agentic tool use. Transformers thrive here because attention lets every token see every other token.
Why these patterns share a shape
All three benefit from learned representations of high-dimensional, redundant data with a lot of latent structure. They are also where pretraining pays back — a model pretrained on the open web has already seen most of the patterns you need; you just have to point it at your task.
The boundary cases worth naming
Deep learning does not automatically beat humans or classical methods at: tabular data with strong feature engineering (gradient boosting still wins), small datasets with no pretraining option, problems where interpretability is a hard requirement, or anything where the cost of being wrong outweighs the cost of being slow. The next lesson is exactly about that boundary.