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Bidirectional recurrent neural networks connect two hidden layers of opposite directions to the same output.

With this form of generative deep learning, the output layer can obtain information about past and future states simultaneously.

The standard recurrent neural network has restrictions because future input information cannot be reached from the current state.

On the contrary, bidirectional recurrent neural networks do not require their input data to be fixed.

Also, their future input information can be accessed from the current state.

Bidirectional recurrent neural networks are particularly useful when the context of the input is needed.