Abstract
In Chapter 6, we covered feedforward neural networks, which are the most basic artificial neural network types. Then, we covered convolutional neural networks in Chapter 7 as the type of artificial neural network architecture, which performs exceptionally good on image data. Now, it is time to cover another type of artificial neural network architecture, recurrent neural network, or RNN, designed particularly to deal with sequential data.
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Notes
- 1.
Text classification with an RNN, TensorFlow, available at www.tensorflow.org/tutorials/text/text_classification_rnn
- 2.
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).
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© 2021 Orhan Gazi Yalçın
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Yalçın, O.G. (2021). Recurrent Neural Networks. In: Applied Neural Networks with TensorFlow 2. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-6513-0_8
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DOI: https://doi.org/10.1007/978-1-4842-6513-0_8
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