Which layer type is commonly used in RNNs for sequence-to-sequence tasks?Question 31Answera.Hidden layerb.Attention layerc.Input layerd.Output layer

Which layer type is responsible for introducing non-linearity in an RNN?Question 22Answera.Activation layerb.Input layerc.Hidden layerd.Output layer

What is the advantage of using recurrent layers in an RNN?Question 21Answera. They can handle variable-length inputsb.   They can generate synthetic datac.They can handle non-linear transformationsd.    They can capture temporal dependencies in the input data

Which of the following is a common architecture used for sequence learning in deep learning? Convolutional Neural Networks (CNNs) Autoencoders Recurrent Neural Networks (RNNs) Generative Adversarial Networks (GANs)

n sequence generation tasks, what is the primary input to an RNN at each time step?Previous outputCurrent weightPrevious errorCurrent inputNone of the given options

Recurrent Artificial Neural NetworksRecurrent Artificial Neural Networks (RNNs) are a type of neural network architecture that is designed to handle sequential data by introducing connections between units in the network that form directed cycles. This cyclic structure allows information to persist over time and enables the network to exhibit dynamic temporal behavior.In contrast to feedforward neural networks, where information flows in one direction from input to output, RNNs have connections that loop back on themselves, allowing them to maintain an internal state or memory of previous inputs. This makes them well-suited for tasks that involve sequential data or time series, such as natural language processing, speech recognition, and time series prediction. The basic unit of an RNN is called a recurrent neuron or a recurrent unit.