Recurrent Neural Network (RNN)

In this video, Professor Khanh discusses recurrent neural networks (RNN) and their application in deep learning. RNNs are commonly used for processing sequence data such as text, audio, or temporal data. The video highlights two important RNN architectures: bidirectional RNN, long short-term memory (LSTM), and gated recurrent unit (GRU).

These architectures have proven to be efficient in various fields, including bioinformatics. The video presents a study that applies RNN (GRU) to learn features from protein sequence data using the PSSM profile. The results demonstrate that GRU outperforms other neural networks, including CNN, in classifying electron transport proteins, achieving an accuracy of 92.3%.

What are the two most important RNN architectures mentioned in the video? How does the RNN architecture allow previous outputs to be used as inputs? What is the advantage of using RNN in the study of protein sequences compared to CNN?