Introduction to Deep Learning
Recurrent Neural Networks (RNNs) are a type of neural network architecture that are designed to handle sequential data. In contrast to feedforward neural networks, which process input data through a series of layers in a single pass, RNNs can use information from previous inputs to inform the processing of current inputs.
An RNN processes sequential data by maintaining a hidden state that represents the network's memory of previous inputs. The hidden state is updated at each time step using the current input and the previous hidden state. This allows the network to capture temporal dependencies in the data, which is useful for tasks like natural language processing and speech recognition.
One of the key challenges in training RNNs is the vanishing gradient problem. Because the gradient of the loss function with respect to the parameters of the network is propagated through the hidden state at each time step, it can become very small or even zero over time, effectively preventing the network from learning long-term dependencies. To address this problem, several types of RNNs have been developed, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, which use gating mechanisms to selectively update the hidden state and avoid the vanishing gradient problem.
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