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What is Deep Neural Network (DNN)

Chapter 2
Smart Systems Design, Applications, and Challenges
An artificial neural network with multiple hidden layers.
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
Abstract
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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Chapter 9
Deep Learning for Moving Object Detection and Tracking
It is a network with more than two layers and the word “deep” refers to the number of layers through which the data is transformed.
Published in Chapter: Deep Learning for Moving Object Detection and Tracking; From: Examining the Impact of Deep Learning and IoT on Multi-Industry Applications
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Chapter 101
Fairness Challenges in Artificial Intelligence
DNNs are ANN of multiple hidden layers.
Published in Chapter: Fairness Challenges in Artificial Intelligence; From: Encyclopedia of Data Science and Machine Learning
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Chapter 127
Deep Learning for Cyber Security Risk Assessment in IIoT Systems
A neural network with multiple hidden layers, which use sophisticated mathematical modeling to process data in a complex way.
Published in Chapter: Deep Learning for Cyber Security Risk Assessment in IIoT Systems; From: Encyclopedia of Data Science and Machine Learning
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Chapter 11
Hybrid Neural Networks for Renewable Energy Forecasting: Solar and Wind Energy Forecasting Using LSTM and RNN
A class of machine learning model. The main difference between Classical and Deep network scheme is the number of the hidden layer and the training process. Using more hidden layers, DNN can extract higher order of interrelation.
Published in Chapter: Hybrid Neural Networks for Renewable Energy Forecasting: Solar and Wind Energy Forecasting Using LSTM and RNN; From: Research Advancements in Smart Technology, Optimization, and Renewable Energy
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Chapter 9
Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry
A DNN’s input and output layers are separated by multiple levels. Neurons, synapses, weights, biases, and functions are crucial components of neural networks, regardless of their size or configuration.
Published in Chapter: Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry; From: Social Customer Relationship Management (Social-CRM) in the Era of Web 4.0
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Chapter 9
Privacy-Centric Approach in Leveraging Federated Learning for Improved Parkinson's Disease Diagnosis
A type of artificial neural network with multiple layers between the input and output layers, capable of learning complex representations from data.
Published in Chapter: Privacy-Centric Approach in Leveraging Federated Learning for Improved Parkinson's Disease Diagnosis; From: Pioneering Smart Healthcare 5.0 with IoT, Federated Learning, and Cloud Security
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Chapter 6
Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)
A technology in the field of Machine Learning that can use statistical learning methods to extract high-level features from original sensory data and obtain an adequate representation of input space in a large amount of data.
Published in Chapter: Protein-Protein Interactions (PPI) via Deep Neural Network (DNN); From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 2
Convolutional Neural Network
An artificial neural network with multiple hidden layers.
Published in Chapter: Convolutional Neural Network; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 5
Deep Learning in Instructional Analysis, Design, Development, Implementation, and Evaluation (ADDIE)
The deep neural network (e.g., DNN) is an artificial neural network (e.g., ANN) with multiple layers between the input and output layers that allow to learn the features that optimally represent the given training data.
Published in Chapter: Deep Learning in Instructional Analysis, Design, Development, Implementation, and Evaluation (ADDIE); From: Applications of Machine Learning and Artificial Intelligence in Education
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