What Is GDP & Why Is It Important?
Implementing gradient clipping with an appropriate threshold helps mitigate the vanishing gradient problem, ensuring stable weight updates during training. Introducing dropout between LSTM layers aids in preventing overfitting by randomly deactivating a fraction of input units during each training iteration. Additionally, utilizing batch normalization normalizes input and recurrent activations, contributing to more stable and accelerated …