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...
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| Формат: | Электронная книга |
| Язык: | English |
| Публикация: |
Birmingham
Packt Publishing, Limited,
2019.
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| Предметы: | |
| Online-ссылка: | EBSCOhost Перейти в каталог НБ ТГУ |
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| 020 | |a 1789616069 | ||
| 020 | |a 9781789616064 |q (electronic bk.) | ||
| 037 | |a B2FC99E5-8AE1-491D-9DAC-923D5AE5B2DE |b OverDrive, Inc. |n http://www.overdrive.com | ||
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| 049 | |a MAIN | ||
| 100 | 1 | |a Razzaque, Mohammad Abdur. |9 913229 | |
| 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. |9 911099 | ||
| 300 | |a 1 online resource (298 pages) | ||
| 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 | |
| 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 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 | ||
| 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. | |
| 653 | 0 | |a Internet of things. | |
| 653 | 7 | |a Internet of things. |2 fast |0 (OCoLC)fst01894151 | |
| 655 | 0 | |a EBSCO eBooks |9 905790 | |
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| 700 | 1 | |a Karim, Md. Rezaul |9 913230 | |
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