What are some common mistakes in deep learning?

What are some common mistakes in deep learning?

Some common mistakes include overfitting, underfitting, not normalizing data, and poor choice of model architecture.

How can I avoid overfitting in my model?

You can avoid overfitting by using techniques like dropout, regularization, and ensuring you have enough training data.

What is the importance of data normalization?

Data normalization helps in speeding up the training process and improving the model's performance by ensuring that all features contribute equally.

Can you explain underfitting?

Underfitting occurs when a model is too simple to capture the underlying patterns in the data, leading to poor performance.