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Hands-On Deep Learning for IoT

You're reading from  Hands-On Deep Learning for IoT

Product type Book
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Pages 308 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Profile icon Dr. Mohammad Abdur Razzaque
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
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 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

The motivation to use DL in IoT data analytics

In recent years, many IoT applications have been actively exploiting sophisticated DL technologies, which use neural networks to capture and understand their environments. Amazon Echo, for example, is considered to be an IoT application as it connects the physical and human world with the digital world; it can understand human voice commands using DL.

Additionally, Microsoft's Windows face-recognition security system (an IoT application) uses DL technology to perform tasks such as unlocking a door when it recognizes its user's face. DL and IoT are among the top three strategic technology trends for 2017, and were announced at the Gartner Symposium/ITxpo 2016. The intensive publicity around DL is due to the fact that traditional machine learning algorithms do not address the emerging analytic needs of IoT systems. On the...

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