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Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications
Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications
상세정보
- 자료유형
- 전자책(국외)
- 미국국회도서관 청구기호
- TK5105.8857
- 기본표목-개인명
- 표제와 책임표시사항
- Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / Mohammad Abdur Razzaque, Md. Rezaul Karim.
- 발행, 배포, 간사 사항
- 형태사항
- 1 online resource (298 pages)
- 내용주기
- 완전내용Cover; 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
- 내용주기
- 완전내용AutoencodersConvolutional 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
- 내용주기
- 완전내용Transfer 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
- 내용주기
- 완전내용Implementing 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
- 내용주기
- 완전내용Example -- Indoor localization with Wi-Fi fingerprinting
- 요약 등 주기
- 요약Reference; 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
- 요약 등 주기
- 요약This book will provide you an overview of Deep Learning techniques to facilitate the analytics and learning in various IoT apps. We will take you through each process - from data collection, analysis, modeling, statistics, and monitoring. We will make IoT data speak with a set of popular frameworks, like TensorFlow, TensorFlow Lite, and Chainer.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 부출표목-개인명
- 기타형태저록
- Print version Karim Rezaul Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications
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MARC
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■24510▼aHands-On Deep Learning for IoT :▼bTrain Neural Network Models to Develop Intelligent IoT Applications /▼cMohammad Abdur Razzaque, Md. Rezaul Karim.
■260 ▼aBirmingham▼bPackt Publishing, Limited▼c2019.
■300 ▼a1 online resource (298 pages)
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■5050 ▼aCover; 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
■5058 ▼aAutoencodersConvolutional 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
■5058 ▼aTransfer 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
■5058 ▼aImplementing 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
■5058 ▼aExample -- Indoor localization with Wi-Fi fingerprinting
■520 ▼aReference; 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
■520 ▼aThis book will provide you an overview of Deep Learning techniques to facilitate the analytics and learning in various IoT apps. We will take you through each process - from data collection, analysis, modeling, statistics, and monitoring. We will make IoT data speak with a set of popular frameworks, like TensorFlow, TensorFlow Lite, and Chainer.
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■7001 ▼aKarim, Md. Rezaul
■77608▼iPrint version▼aKarim, Rezaul.▼tHands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications.▼dBirmingham : Packt Publishing, Limited, ©2019▼z9781789616132
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