What are the main challenges of deep learning?

What are the main challenges of deep learning?

Some key challenges include overfitting, data quality, computational resources, and interpretability.

Can you explain overfitting and how to prevent it?

Overfitting occurs when a model learns noise instead of the signal. Techniques like regularization and cross-validation can help.

What about the need for large datasets?

Yes, deep learning models typically require large amounts of data to generalize well. Data augmentation can be a solution.

How does interpretability affect deep learning models?

Interpretability is crucial for trust and accountability, especially in sensitive applications like healthcare.