Deep learning is a branch of machine learning that uses neural networks with many layers. Both families share the same goal — learning from data — but they differ in complexity, in the data they need and in how interpretable they are.
Where they differ
- Feature engineering: classic machine learning needs hand-crafted features; deep learning discovers its own from raw data
- Data and computing power: neural networks need very large datasets and powerful hardware
- Interpretability: classic models are easier to trace; deep models are harder to explain
- Performance: on large, unstructured inputs such as whole images, deep learning usually wins
In practice the two complement each other. For structured clinical tables, classic algorithms are often the best, most transparent choice. For images, sounds and text, deep networks dominate. The choice is a balance between accuracy, evidence and explainability.
Both are machine learning. “Deep” simply means many layers of computation.