Recurrent Neural Networks (RNNs) are a type of neural network commonly used for processing time series data, such as speech, text, or stock prices. RNNs are effective at processing sequential data because they can maintain a “memory” of past inputs and use that information to make predictions about future inputs.
RNNs consist of a series of interconnected “memory” cells, each with a set of weights that are learned during training. Each memory cell receives an input and an output from the previous cell, and uses that information to update its internal state. The output of the last cell is then used to make a prediction about the next input.
One of the key advantages of RNNs is their ability to handle variable-length input sequences. This makes them well-suited to tasks such as speech recognition, where the length of the input sequence can vary from one utterance to the next. RNNs can also be used in combination with other neural network architectures, such as CNNs or autoencoders, to process complex input data.
However, RNNs suffer from the problem of vanishing gradients, where the gradients of the loss function with respect to the weights of the earlier layers become very small during training. This can make it difficult to train RNNs effectively, especially for long sequences.
To address this problem, several variants of RNNs have been developed, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). These variants use specialized memory cells that can better maintain long-term dependencies in the input sequence, making them more effective for processing long sequences.
RNNs have been highly successful in a variety of time series processing tasks, including speech recognition, natural language processing, and stock price prediction. They have also been used in applications such as handwriting recognition, music generation, and machine translation.