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  ▼a  1107335469

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  ▼a  1789616069

• 020

  ▼a  9781789616064
  ▼q  (electronic bk.)

• 035

  ▼a  2179553
  ▼b  (N$T)

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  ▼a  (OCoLC)1107574315
  ▼z  (OCoLC)1107335469

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  ▼a  B2FC99E5-8AE1-491D-9DAC-923D5AE5B2DE
  ▼b  OverDrive, Inc.
  ▼n  http://www.overdrive.com

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  ▼a  EBLCP
  ▼b  eng
  ▼e  pn
  ▼c  EBLCP
  ▼d  TEFOD
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  ▼d  OCLCQ
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  ▼d  248023

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  ▼a  TK5105.8857

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  ▼a  005.8
  ▼2  23

• 100

  ▼a  Razzaque, Mohammad Abdur.

• 245

  ▼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
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  ▼a  computer
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  ▼a  online resource
  ▼b  cr
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• 505

  ▼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

  ▼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

  ▼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

  ▼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

  ▼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

  ▼a  Print version record.

• 590

  ▼a  Added to collection customer.56279.3

• 650

  ▼a  Internet of things.

• 650

  ▼a  Internet of things.
  ▼2  fast
  ▼0  (OCoLC)fst01894151

• 655

  ▼a  Electronic books.

• 700

  ▼a  Karim, Md. Rezaul

• 776

  ▼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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  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2179553

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  ▼a  EBL - Ebook Library
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  ▼n  EBL5806407

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  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  300674933

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  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2179553

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  ▼a  강리원

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  ▼a  eBook

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  ▼b  N$T