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What is Deep Belief Network

Smart Systems Design, Applications, and Challenges
A probabilistic generative model with multiple layers of the so called latent variables tha keep the state of the network.
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.
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An Insight Into Deep Learning Architectures
It is the type of deep neural network which is an unsupervised generative model.
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Building a Conversational Chatbot Using Machine Learning: Towards a More Intelligent Healthcare Application
A type of generative model that is composed of multiple layers of Restricted Boltzmann Machines (RBMs) stacked on top of each other.
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