What are the best practices for using Keras in deep learning?

What are the best practices for using Keras in deep learning?

Some best practices include using the Functional API for complex models, normalizing your data, and using callbacks.

Can you explain why normalizing data is important?

Normalizing data helps in speeding up the training process and achieving better convergence by ensuring that all features contribute equally.

What are some useful callbacks I should consider?

You should consider using EarlyStopping, ModelCheckpoint, and ReduceLROnPlateau to improve your training process.

How can I implement these callbacks in my Keras model?

You can implement them by importing the callbacks from keras.callbacks and passing them to the fit method of your model.