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Hands-On Deep Learning for IoT

You're reading from   Hands-On Deep Learning for IoT Train neural network models to develop intelligent IoT applications

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Product type Paperback
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Length 308 pages
Edition 1st Edition
Languages
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Authors (3):
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Aditya Trivedi Aditya Trivedi
Author Profile Icon Aditya Trivedi
Aditya Trivedi
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Author Profile Icon Dr. Mohammad Abdur Razzaque
Dr. Mohammad Abdur Razzaque
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT FREE CHAPTER 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

Potential future solutions

In this section, we shall briefly discuss a few potential research and development solutions in order to address some of the issues mentioned:

  • Distributed learning: In this book, the model learning or training were done centrally, but this may not be feasible in many IoT applications. In this context, distributed learning can be a potential solution. However, distributed computing has a security problem, and this can be minimized through Blockchain-based Distributed Learning.
  • IoT mobile data: Smartphones are a key contributor of IoT proliferation. Efficient solutions for mobile big data analysis through DL models can offer better IoT services in various application domains, including smart cities. This area needs further research.
  • Integration of contextual information: Contextual information is essential for correctly using and interpreting DL-based...
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