TY - CHAP
T1 - Recurrent Neural Networks (RNNs)
T2 - Architectures, Training Tricks, and Introduction to Influential Research
AU - Das, Susmita
AU - Tariq, Amara
AU - Santos, Thiago
AU - Kantareddy, Sai Sandeep
AU - Banerjee, Imon
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - Recurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.
AB - Recurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.
KW - Bidirectional RNN (BRNN)
KW - Deep RNN
KW - GRU
KW - LSTM
KW - Language modeling
KW - Recurrent neural network (RNN)
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U2 - 10.1007/978-1-0716-3195-9_4
DO - 10.1007/978-1-0716-3195-9_4
M3 - Chapter
AN - SCOPUS:85172003729
T3 - Neuromethods
SP - 117
EP - 138
BT - Neuromethods
PB - Humana Press Inc.
ER -