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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

Summary

Automatic audio/speech/voice recognition is becoming a popular means for people to interact with their devices, including smartphones, wearables, and other smart devices. Machine learning and DL algorithms are essential in audio/speech/voice-based decision making.

In the first part of this chapter, we briefly described different IoT applications and their audio/speech/voice detection-based decision making. We also briefly discussed two potential use cases of IoT where DL algorithms can be useful in speech/command-based decision making. The first use case considered an IoT application to make a home smart using voice-controlled lighting. The second use case also made a home or office smart, where a DL-based IoT solution offered automated access control to the smart home or office. In the second part of the chapter, we briefly discussed the data collection process for the...

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