Deep Learning vs. Machine Learning: What's the Difference?
Deep learning is a specialized subset of machine learning. Machine learning is any system that learns patterns from data; deep learning is the branch that uses large, multi-layered neural networks to learn complex patterns — and it powers most modern AI breakthroughs.
See the difference, explained visually.
Watch a 2-minute animated lesson comparing deep learning and machine learning.
At a glance
| Deep Learning | Machine Learning | |
|---|---|---|
| Relationship | A subset of machine learning | The broader field |
| Method | Many-layered neural networks | Many techniques, including deep learning |
| Data needs | Usually large datasets | Can work with smaller data |
| Features | Learns them automatically | Often needs hand-picked features |
| Powers | Image, speech, and language models | Spam filters, recommendations, forecasting |
Which should you use?
Deep Learning
Deep learning shines on complex, high-dimensional data — images, audio, language — given enough data and compute.
Machine Learning
Classic machine learning is often better for smaller datasets or simpler, structured problems, and is cheaper to run.
Frequently asked questions
- Is deep learning the same as machine learning?
- No — deep learning is a subset. All deep learning is machine learning, but machine learning also includes many simpler methods that don't use deep neural networks.
- When is deep learning overkill?
- For small datasets or simple, structured problems, classic machine learning is often faster, cheaper, and just as accurate — deep learning's edge shows with lots of complex data.
- How do they relate to AI?
- AI is the broadest term; machine learning is one approach to AI; deep learning is one approach to machine learning. Each is nested inside the last.