What are some common pitfalls when using AI frameworks?

What are some common pitfalls when using AI frameworks?

Some common pitfalls include overfitting, lack of data preprocessing, and not understanding the framework's limitations.

Can you explain overfitting and how to avoid it?

Overfitting occurs when a model learns the training data too well, including noise. To avoid it, use techniques like cross-validation and regularization.

What about data preprocessing? Why is it important?

Data preprocessing is crucial as it ensures the data is clean and formatted correctly, which can significantly impact model performance.

Are there specific frameworks that are more prone to these pitfalls?

Yes, some frameworks may have default settings that lead to overfitting or poor data handling. It's essential to understand each framework's nuances.