A Study on Working and Applications of Sequential Deep Learning Models

This research study intends to explore the field of sequence-to-sequence and language models. To determine how this research helped towards the new era of artificial general intelligence. RNN, Bi-RNN, LSTM, Bi-LSTM, Encoder and Decoder, Bert, Attention Models, GPT, GPT-2, GPT -3, GPT -4 are some of the sequence models and transformers that are utilized in performing various natural language processing tasks like text summarization, named entity recognition, chatbots, question answering. The essential factor of sequence models is that the data processing is not more evenly and independently widely dispersed samples; rather, the data have some degree of dependence since they are processed in a sequential manner. Mainly this models deals with the text data as input. The applicability of sequential models in many domains will be discussed.