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▼a 9781789616064
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▼a MAIN |
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▼a TK5105.8857 |
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| 100 | 1 |
▼a Razzaque, Mohammad Abdur. |
| 245 | 10 |
▼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. |
| 300 | |
▼a 1 online resource (298 pages) |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
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▼a online resource
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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 |
| 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. |
| 590 | |
▼a Added to collection customer.56279.3 |
| 650 | 0 |
▼a Internet of things. |
| 650 | 7 |
▼a Internet of things.
▼2 fast
▼0 (OCoLC)fst01894151 |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Karim, Md. Rezaul |
| 776 | 08 |
▼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, 짤2019
▼z 9781789616132 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2179553 |
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