Deep Learning Applications with Practical Measured Results in Electronics Industries MDPI AG Author
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Deep Learning Applications with Practical Measured Results in Electronics Industries MDPI AG Author
~EN HC NWISBN: 9783039288632 bzw. 3039288636, vermutlich in Englisch, Mdpi AG, gebundenes Buch, neu.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd, zzgl. Versandkosten.
This book collects 14 articles from the Special Issue entitled Deep Learning Applications with Practical Measured Results in Electronics Industries of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.
This book collects 14 articles from the Special Issue entitled Deep Learning Applications with Practical Measured Results in Electronics Industries of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.
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Deep Learning Applications with Practical Measured Results in Electronics Industries
~EN HC NWISBN: 3039288636 bzw. 9783039288632, vermutlich in Englisch, MDPI AG, gebundenes Buch, neu.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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Deep Learning Applications with Practical Measured Results i
~EN HC NWISBN: 9783039288632 bzw. 3039288636, vermutlich in Englisch, gebundenes Buch, neu.
Lieferung aus: Deutschland, Next Day, Versandkostenfrei.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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