Time series data can also be forecasted using RNNs. Because RNNs have internal memory, they are especially useful for machine learning applications that need sequential input. In Natural Language Processing, RNNs are frequently used (NLP). RNN stands for Recurrent Neural Network and is a Deep Learning and Artificial Neural Network design that is suited for sequential data processing. Different Sequential Model RNN and its Variants Based Models To deal with such data there are some sequential models available and you might have heard some of those. In order to efficiently model with this data or to get as much information, it contains a traditional machine algorithm that will not help as much. Recurrent Neural Network for Predicting Transcription Factor Binding Sites based on DNA Sequence Analysis.Recurrent neural networks are being used to create classical music.Deep Recurrent Neural Network for Speech Recognition Deep Recurrent Neural Network for Speech Recognition.Image captioning is assessing the current action and creating a caption for the image.Machine Translation: Given a single language input, sequence models are used to translate the input into several languages.
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