Description:
Recurrent Neural Networks (RNNs) are a type of neural network architecture specifically designed to handle sequential data. RNNs are well-suited for tasks such as natural language processing (NLP), speech recognition, machine translation, and time series analysis.
The key feature of RNNs is their ability to maintain a hidden state that captures information from previous inputs in the sequence. This hidden state is updated at each time step and serves as the memory of the network, allowing it to capture long-term dependencies and context information.
@Kumar-laxmi please assign me this issue under SSOC'23 in python, java and c++.
Description: Recurrent Neural Networks (RNNs) are a type of neural network architecture specifically designed to handle sequential data. RNNs are well-suited for tasks such as natural language processing (NLP), speech recognition, machine translation, and time series analysis.
The key feature of RNNs is their ability to maintain a hidden state that captures information from previous inputs in the sequence. This hidden state is updated at each time step and serves as the memory of the network, allowing it to capture long-term dependencies and context information.
@Kumar-laxmi please assign me this issue under SSOC'23 in python, java and c++.