Hands-On Deep Learning for IoT: Train Neural Network Models to Develop Intelligent IoT Applications/ Mohammad Abdur Razzaque, Md. Rezaul Karim.

Karim  Md. Rezaul; Razzaque  Mohammad Abdur.

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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