Memory, parameter sharing and Turing completeness, so RNN has certain advantages in learning the nonlinear characteristics of sequences. RNN is applied in natural language processing (NLP), such as speech recognition, language modeling, machinetranslation and other fields. It is also used in various time series prediction.
Input variable: time series data
Output variable: Training data
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 firstname.lastname@example.org
LinksNational Tibetan Plateau Data Center