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What is Long-short Term Memory Network

Smart Systems Design, Applications, and Challenges
A variation of recurrent neural networks to reduce the vanishing problem.
Published in Chapter:
Deep Learning on Edge: Challenges and Trends
Mário P. Véstias (INESC-ID, ISEL, Instituto Politécnico de Lisboa, Portugal)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/978-1-7998-2112-0.ch002
Deep learning on edge has been attracting the attention of researchers and companies looking to provide solutions for the deployment of machine learning computing at the edge. A clear understanding of the design challenges and the application requirements are fundamental to understand the requirements of the next generation of edge devices to run machine learning inference. This chapter reviews several aspects of deep learning: applications, deep learning models, and computing platforms. The way deep learning is being applied to edge devices is described. A perspective of the models and computing devices being used for deep learning on edge are given, as well as what challenges face the hardware designers to guarantee the vast set of tight constraints like performance, power consumption, flexibility, etc. of edge computing platforms. Finally, a trends overview of deep learning models and architectures is discussed.
Full Text Chapter Download: US $37.50 Add to Cart
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Efficient End-to-End Asynchronous Time-Series Modeling With Deep Learning to Predict Customer Attrition
A type of recurrent neural network where regular neurons are replaced by long-short term memory units designed to allow deep networks to “remember” inputs over larger number of steps and mitigate the exploding/vanishing gradient problem.
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