What are some common errors in deep learning?

What are some common errors in deep learning?

Some common errors include overfitting, underfitting, and poor data preprocessing.

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 regularization, dropout, or early stopping.

What about underfitting? How is it different?

Underfitting happens when a model is too simple to capture the underlying patterns in the data. It can be addressed by increasing model complexity or training longer.

What role does data preprocessing play in deep learning?

Data preprocessing is crucial as it helps clean and format the data, ensuring the model learns effectively from high-quality inputs.