What are the main challenges in training neural networks?

What are the main challenges in training neural networks?

Some key challenges include overfitting, underfitting, vanishing gradients, and the need for large datasets.

Can you explain overfitting and how to prevent it?

Overfitting occurs when a model learns the training data too well, including noise. To prevent it, you can use techniques like regularization, dropout, and early stopping.

What is the vanishing gradient problem?

The vanishing gradient problem happens when gradients become too small for effective learning in deep networks, making training slow or ineffective.

How important is data quality in training neural networks?

Data quality is crucial; poor quality data can lead to inaccurate models and unreliable predictions.