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xuan q deep learning applications computer vision signals networks 2023

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Textbook in PDF format Deep learning, first introduced by Hinton et al. in 2006,1 has brought great changes to the world. Modern deep learning technique relies upon deep neuron networks (DNN), which have a very long history and aim to mimic the functionality of human brains. After more than 15 years of development, it has become a great success. In many application areas, deep learning, for the first time in human history, has surpassed even human specialists. Naturally, people are eager to apply such a great and efficient tool to various application fields. The foremost application targets vision-based tasks. Historically, before the emergence of deep learning, no machine learning algorithm could match what the human eye and brain can do. Since deep learning has similar architecture as the human brain we expect it to have sim ilar performance as humans. Preface Introduction Part I: Vision Applications Vision-Based Particulate Matter Estimation Automatic Ship Plate Recognition Using Deep Learning Techniques Generative Adversial Network Enhanced Bearing Roller Defect Detection and Segmentation Application of Deep Learning in Crop Stress Part II: Signal Applications A Mixed Pruning Method for Signal Modulation Recognition Based on Convolutional Neural Network Broad Learning System Based on Gramian Angular Field for Time Series Classification Denoising of Radio Modulation Signal Based on Deep Learning A Graph Neural Network Modulation Recognition Framework Based on Local Limited Penetrable Visibility Graph Part III: Network Applications Study of Autonomous System Business Types Based on Graph Neural Networks Social Media Opinions Analysis Ethereum’s Ponzi Scheme Detection Work Based on Graph Ideas Research on Prediction of Molecular Biological Activity Based on Graph Convolution

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Xuan Q. Deep Learning Applications. In Computer Vision,Signals and Networks 2023.pdf 42.5 MB

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xuan q deep learning applications computer vision signals networks 2023

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