Recurrent Neural Networks Quiz

A type of artificial neural network specifically designed to handle sequential data, like text or time series. It uses internal memory to process information in order, allowing it to learn relationships between elements in a sequence.

A challenge in using standard neural networks for sequential data, where inputs and outputs can have varying lengths. Standard networks struggle to capture relationships across different positions in the sequence.

Another drawback of using standard neural networks for sequential data. They fail to share features learned at one position with other positions in the sequence, limiting their ability to generalize.

The process of calculating the output of an RNN based on the input and the previous hidden state. It involves propagating information forward through the network.

A simplified way to represent the calculations inside an RNN. It shows how the current hidden state is influenced by the previous hidden state and current input.

The process of adjusting the weights in an RNN to improve its performance. It involves backpropagating error signals through time, updating weights based on their contribution to the errors at each step.

The ability of an RNN to remember information from previous steps, allowing it to understand the context of current data. This is crucial for tasks involving sequences, such as language translation or sentiment analysis.

A simple representation of an RNN's hidden state at different time steps, often used for visualizing how information flows through the network over time.

A key component of recurrent neural networks that helps them handle sequences of varying lengths. They store information about past inputs and can be accessed by future steps in the sequence.

A subtype of RNN designed specifically for processing sequences of varying lengths. They can handle variable-length inputs and outputs, making them useful for tasks like machine translation and text summarization.

A type of RNN where the input sequence is processed in a forward direction, but the outputs are only generated at the end of the sequence. They are commonly used for tasks like sentiment classification and image captioning.

A type of RNN where each input in the sequence produces a corresponding output. They are suitable for tasks like music generation and speech recognition.

A type of RNN where multiple inputs are processed to generate multiple outputs. They handle scenarios where the length of the output sequence depends on the length of the input sequence. Useful for tasks like machine translation and text summarization.

A type of RNN where the output at each time step depends on the previous output and the current input. This makes them suitable for generating sequences where the elements are interdependent, like music or text.

A type of recurrent neural network that effectively addresses the vanishing gradient problem encountered in traditional RNNs. GRUs introduce a "gating mechanism" to control the flow of information, allowing the network to remember long-term dependencies in sequential data.

A variant of RNNs that excels at capturing long-range dependencies in sequential data. LSTMs incorporate a more sophisticated "gating mechanism" than GRUs, containing three gates: the input, forget, and output gates. This allows for a finer control of information flow, enhancing the network's ability to remember relevant information over extended periods.

A common issue in training RNNs where the gradient signal diminishes exponentially as it propagates through the network's layers. This results in poor learning of long-term dependencies in sequential data.

A phenomenon where the gradient signal in an RNN explodes, growing excessively large during training. This can lead to instability and hinder the learning process.

A specialized unit within an RNN that processes sequential data. It's like a 'memory' cell that stores and updates information from the previous time step, allowing the network to consider past context.

The ability of a network to capture relationships between distant elements in a sequence. For example, remembering the beginning of a sentence to understand its meaning.

A mechanism within GRUs and LSTMs that controls the flow of information. These gates act as filters that decide which information is passed through.

The ability of an RNN to remember information for extended periods, even after processing many time steps. This capacity is crucial for tasks involving long sequences.