Recurrent Neural Networks (RNNs)

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Authors

Agha Fahad Khan¹

1. Riphah International University Lahore campus

Conference / event

2nd Workshop on Advancements of Mathematics & its Applications (WAMA-2024 ), June 2024 (Lahore, Pakistan)

Poster summary

Recurrent Neural Networks (RNNs) Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to recognize patterns in sequences of data, such as time series, speech, text, or even video. Unlike traditional neural networks, RNNs have loops, allowing information to persist. This makes them uniquely suited for tasks where context and order are crucial. Imagine trying to understand a story one word at a time without remembering the previous words. That's where RNNs shine—they can retain memory of what has come before, making them powerful tools for language translation, speech recognition, and predicting stock prices. However, they come with challenges like vanishing gradients, which can make training difficult. Despite this, advances like Long Short-Term Memory (LSTM) units and Gated Recurrent Units (GRUs) have significantly improved their performance. RNNs bring machines a step closer to understanding sequences as humans do, enabling more natural interactions and smarter predictions.

Keywords

Machine Learning, Neural Network, Recurrent Neural Networks (RNNs), LSTM, Artificial Neural network

Research areas

Mathematics, Computer and Information Science

References

1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.Hochreiter, S., & Schmidhuber, J. (1997).

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license

Copyright © 2024 Fahad Khan. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.