What is the Difference between ‘Dense’ and ‘TimeDistributedDense’ of Keras.

Answer: Dense in Keras applies fully connected layers to the last output dimension, whereas TimeDistributedDense applies the same dense layer independently to each time step of a sequence input.

Here’s a detailed comparison between Dense and TimeDistributedDense in Keras:

Aspect	Dense Layer	TimeDistributedDense Layer
Purpose	Applies a fully connected (dense) layer to the input data.	Applies a dense layer independently to each time step of a sequence input.
Input Shape	Expects 2D tensors (batch_size, features).	Expects 3D tensors (batch_size, timesteps, features).
Output Shape	Produces 2D tensors (batch_size, units) where units is the number of neurons in the dense layer.	Produces 3D tensors (batch_size, timesteps, units) where units is the number of neurons in the dense layer.
Use Case	Suitable for independent feature transformations across all inputs.	Suitable for sequence processing tasks where each time step requires its own transformation.
Example	model.add(Dense(64)) would add a dense layer with 64 output neurons to the model.	model.add(TimeDistributedDense(64)) would add a dense layer with 64 output neurons that operates on each time step of a sequence input.

Key differences:

• Time dimension: Dense operates on a single time step, while TimeDistributedDense processes each time step individually.
• Output shape: Dense outputs a 2D tensor, while TimeDistributedDense preserves the sequence dimension in the output.
• Use case: Dense is for non-sequential data or final layers, while TimeDistributedDense was specifically for applying Dense to each time step (now replaced by Dense with return_sequences=True).

Conclusion:

In summary, Dense layers are used when you want to apply the same transformation to every element in your input, regardless of its position in the sequence. On the other hand, TimeDistributedDense layers are used when you have sequential data and you want to apply different transformations at each time step.