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Hands-On Deep Learning for IoT: Train Neural Network Models to Develop Intelligent IoT Applications/ Mohammad Abdur Razzaque, Md. Rezaul Karim.
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| dc.contributor.author | Razzaque Mohammad Abdur. | |
| dc.contributor.author | Karim Md. Rezaul | |
| dc.date.accessioned | 2024-01-26T21:32:00Z | |
| dc.date.available | 2024-01-26T21:32:00Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Razzaque и др. Hands-On Deep Learning for IoT: Train Neural Network Models to Develop Intelligent IoT Applications - Birmingham: Packt Publishing, Limited, 2019 - 1 online resource (298 pages) - URL: https://libweb.kpfu.ru/ebsco/pdf/2179553.pdf | |
| dc.identifier.isbn | 1789616069 | |
| dc.identifier.isbn | 9781789616064 | |
| dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/178231 | |
| dc.description.abstract | Reference; Chapter 2: Deep Learning Architectures for IoT; A soft introduction to ML; Working principle of a learning algorithm; General ML rule of thumb; General issues in ML models; ML tasks; Supervised learning; Unsupervised learning; Reinforcement learning; Learning types with applications; Delving into DL; How did DL take ML to the next level?; Artificial neural networks; ANN and the human brain; A brief history of ANNs; How does an ANN learn?; Training a neural network; Weight and bias initialization; Activation functions; Neural network architectures; Deep neural networks | |
| dc.description.tableofcontents | Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks; Chapter 1: The End-to-End Life Cycle of the IoT; The E2E life cycle of the IoT; The three-layer E2E IoT life cycle; The five-layer IoT E2E life cycle; IoT system architectures; IoT application domains; The importance of analytics in IoT; The motivation to use DL in IoT data analytics; The key characteristics and requirements of IoT data; Real-life examples of fast and streaming IoT data; Real-life examples of IoT big data | |
| dc.description.tableofcontents | AutoencodersConvolutional neural networks; Recurrent neural networks; Emergent architectures; Residual neural networks; Generative adversarial networks; Capsule networks; Neural networks for clustering analysis; DL frameworks and cloud platforms for IoT; Summary; Section 2: Hands-On Deep Learning Application Development for IoT; Chapter 3: Image Recognition in IoT; IoT applications and image recognition; Use case one -- image-based automated fault detection; Implementing use case one; Use case two -- image-based smart solid waste separation; Implementing use case two | |
| dc.description.tableofcontents | Transfer learning for image recognition in IoTCNNs for image recognition in IoT applications; Collecting data for use case one; Exploring the dataset from use case one; Collecting data for use case two; Data exploration of use case two; Data pre-processing; Models training; Evaluating models; Model performance (use case one); Model performance (use case two); Summary; References; Chapter 4: Audio/Speech/Voice Recognition in IoT; Speech/voice recognition for IoT; Use case one -- voice-controlled smart light; Implementing use case one; Use case two -- voice-controlled home access | |
| dc.description.tableofcontents | Implementing use case twoDL for sound/audio recognition in IoT; ASR system model; Features extraction in ASR; DL models for ASR; CNNs and transfer learning for speech recognition in IoT applications; Collecting data; Exploring data; Data preprocessing; Models training; Evaluating models; Model performance (use case 1); Model performance (use case 2); Summary; References; Chapter 5: Indoor Localization in IoT; An overview of indoor localization; Techniques for indoor localization; Fingerprinting; DL-based indoor localization for IoT; K-nearest neighbor (k-NN) classifier; AE classifier | |
| dc.description.tableofcontents | Example -- Indoor localization with Wi-Fi fingerprinting | |
| dc.language | English | |
| dc.language.iso | en | |
| dc.publisher | Birmingham Packt Publishing, Limited | |
| dc.subject.other | Internet of things. | |
| dc.subject.other | Internet of things. | |
| dc.subject.other | Electronic books. | |
| dc.title | Hands-On Deep Learning for IoT: Train Neural Network Models to Develop Intelligent IoT Applications/ Mohammad Abdur Razzaque, Md. Rezaul Karim. | |
| dc.type | Book | |
| dc.description.pages | 1 online resource (298 pages) | |
| dc.collection | Электронно-библиотечные системы | |
| dc.source.id | EN05CEBSCO05C347 |
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