Recurrent Neural Networks (RNNs)

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作者

Agha Fahad Khan¹

1. Riphah International University Lahore campus

会议/活动

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

海报摘要

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.

关键词

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

研究领域

Mathematics, Computer and Information Science

参考文献

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

基金

暂无数据

补充材料

暂无数据

附加信息

利益冲突

No competing interests were disclosed.

数据可用性声明

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

知识共享许可协议

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.