Recurrent layers are a type of neural network layer that are used to process sequential data, such as time series data, text, and speech. They are designed to capture temporal dependencies in the input data by maintaining a hidden state, which is updated at each time step. The basic idea behind recurrent layers is to use the same set of weights for each time step in the input sequence, rather than having a different set of weights for each time step like in feedforward neural networks. This allows the model to learn temporal dependencies in the data by passing information through the hidden state from one time step to the next. The hidden state is updated at each time step by combining the current input and the previous hidden state using a set of weights, known as the recurrent weights. This operation is often called the recurrent step, and it is typically implemented using an element-wise non-linear function, such as a sigmoid or a tanh function. There are several types of recurrent layers, such as Simple Recurrent Layers, LSTM(Long Short-term memory), and GRU(Gated Recurrent Unit). These layers differ in the way they maintain the hidden state and update it at each time step, but they all share the same basic idea of using the same set of weights for each time step in the input sequence to capture temporal dependencies in the data. In summary, Recurrent layers are a type of neural network layer that are used to process sequential data, such as time series data, text, and speech. They are designed to capture temporal dependencies in the input data by maintaining a hidden state, which is updated at each time step. They use the same set of weights for each time step in the input sequence, allowing the model to learn temporal dependencies in the data by passing information through the hidden state from one time step to the next. There are several types of recurrent layers, such as Simple Recurrent Layers, LSTM, and GRU.
neural networks, hidden state, sequential data
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