A statistical journey to DNN, the second trip: Architecture of RNN and image classification

RNNs are models that play a pivotal role in understanding various forms of DNNs.They have evolved into Seq2Seq models and subsequently into Transformers, leading to the development of large language models (LLMs) that are currently the focus of significant interest.Nonetheless, understanding the operation of RNNs is not an easy task.In particular, the core models of RNNs, LSTM and GRU, are challenging to comprehend due to their structural complexity.This paper explores ways to understand the operation of LSTM and GRU.Additionally, to demonstrate specific use cases of LSTM and GRU, we applied them to the problem of handwritten digit classification using the MNIST dataset.We utilized a method of segmenting each image into multiple patches and applied bidirectional LSTM and bidirectional GRU.The results were then compared with those of CNN.