What are common errors when using LSTM models?

What are some common errors I should watch out for when using LSTM models?

Some common errors include improper data preprocessing, not tuning hyperparameters, and vanishing gradients.

Can you explain what vanishing gradients are?

Vanishing gradients occur when gradients become too small for the model to learn effectively, especially in deep networks.

How can I prevent vanishing gradients in my LSTM?

You can use techniques like gradient clipping, proper weight initialization, and using activation functions like ReLU.

What about overfitting? How can I avoid that?

To avoid overfitting, you can use regularization techniques, dropout layers, and ensure you have enough training data.