RNNs are a type of neural network that allow information to persist across multiple time steps in a sequence.
RNN is suitable for time-series analysis and NLP tasks.
The main characteristic of RNNs is their ability to use the output of a previous step as input to the current step.
RNNs have a "memory" in the form of hidden states that can retain information from previous time steps.
RNNs can be optimized using gradient descent algorithms such as Adam and RMSprop
In time-series analysis, RNNs can be used for forecasting, anomaly detection, and classification
In NLP, RNN is used in task such as language modeling, machine translation, and speech recognition
RNNs have achieved state-of-the-art results in several NLP tasks such as language modeling, machine translation, and sentiment analysis.
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