Hands-On Deep Learning for IoT :: Train Neural Network Models to Develop Intelligent IoT Applications /
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; Delv...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 This 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. |
| Beschreibung: | 1 online resource (298 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 1789616069 9781789616064 |
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| 245 | 1 | 0 | |a Hands-On Deep Learning for IoT : |b Train Neural Network Models to Develop Intelligent IoT Applications / |c Mohammad Abdur Razzaque, Md. Rezaul Karim. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
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| 505 | 0 | |a 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 | |
| 520 | |a 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 | ||
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a Example -- Indoor localization with Wi-Fi fingerprinting | |
| 520 | |a This 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. | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references. | ||
| 650 | 0 | |a Internet of things. |0 http://id.loc.gov/authorities/subjects/sh2013000266 | |
| 650 | 6 | |a Internet des objets. | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Pattern recognition. |2 bicssc | |
| 650 | 7 | |a Computer vision. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Internet of things |2 fast | |
| 700 | 1 | |a Karim, Md. Rezaul | |
| 776 | 0 | 8 | |i Print version: |a Karim, Rezaul. |t Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781789616132 |
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| contents | 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 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 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 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 Example -- Indoor localization with Wi-Fi fingerprinting |
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| spelling | Razzaque, Mohammad Abdur. Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / Mohammad Abdur Razzaque, Md. Rezaul Karim. Birmingham : Packt Publishing, Limited, 2019. 1 online resource (298 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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 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 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 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 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 Example -- Indoor localization with Wi-Fi fingerprinting This 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. Print version record. Includes bibliographical references. Internet of things. http://id.loc.gov/authorities/subjects/sh2013000266 Internet des objets. Artificial intelligence. bicssc Pattern recognition. bicssc Computer vision. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Computer Vision & Pattern Recognition. bisacsh Computers Neural Networks. bisacsh Internet of things fast Karim, Md. Rezaul Print version: Karim, Rezaul. Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications. Birmingham : Packt Publishing, Limited, ©2019 9781789616132 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2179553 Volltext |
| spellingShingle | Razzaque, Mohammad Abdur Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / 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 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 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 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 Example -- Indoor localization with Wi-Fi fingerprinting Internet of things. http://id.loc.gov/authorities/subjects/sh2013000266 Internet des objets. Artificial intelligence. bicssc Pattern recognition. bicssc Computer vision. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Computer Vision & Pattern Recognition. bisacsh Computers Neural Networks. bisacsh Internet of things fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2013000266 |
| title | Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / |
| title_auth | Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / |
| title_exact_search | Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / |
| title_full | Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / Mohammad Abdur Razzaque, Md. Rezaul Karim. |
| title_fullStr | Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / Mohammad Abdur Razzaque, Md. Rezaul Karim. |
| title_full_unstemmed | Hands-On Deep Learning for IoT : Train Neural Network Models to Develop Intelligent IoT Applications / Mohammad Abdur Razzaque, Md. Rezaul Karim. |
| title_short | Hands-On Deep Learning for IoT : |
| title_sort | hands on deep learning for iot train neural network models to develop intelligent iot applications |
| title_sub | Train Neural Network Models to Develop Intelligent IoT Applications / |
| topic | Internet of things. http://id.loc.gov/authorities/subjects/sh2013000266 Internet des objets. Artificial intelligence. bicssc Pattern recognition. bicssc Computer vision. bicssc Neural networks & fuzzy systems. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Computer Vision & Pattern Recognition. bisacsh Computers Neural Networks. bisacsh Internet of things fast |
| topic_facet | Internet of things. Internet des objets. Artificial intelligence. Pattern recognition. Computer vision. Neural networks & fuzzy systems. Computers Intelligence (AI) & Semantics. Computers Computer Vision & Pattern Recognition. Computers Neural Networks. Internet of things |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2179553 |
| work_keys_str_mv | AT razzaquemohammadabdur handsondeeplearningforiottrainneuralnetworkmodelstodevelopintelligentiotapplications AT karimmdrezaul handsondeeplearningforiottrainneuralnetworkmodelstodevelopintelligentiotapplications |