Kernel Methods vs Deep Learning: Which is Better?

What are kernel methods and how do they compare to deep learning?

Kernel methods are a class of algorithms for pattern analysis, while deep learning uses neural networks. Both have their strengths.

Can you explain the advantages of kernel methods over deep learning?

Kernel methods are often more interpretable and require less data to train effectively, especially for smaller datasets.

What about the scalability of deep learning compared to kernel methods?

Deep learning excels in scalability and can handle large datasets efficiently, making it suitable for complex tasks.

Are there specific scenarios where one is preferred over the other?

Yes, kernel methods are preferred for smaller, structured data, while deep learning is better for unstructured data like images and text.