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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

Summary

In this chapter, we presented two different layered views of the E2E life cycle of the IoT. We also looked at the IoT system architecture and key application domains of IoT. Following this, we defined what is meant by IoT analytics and its importance in IoT applications, with special emphasis on DL. We discussed the key characteristics of IoT and their corresponding requirements in analytics. Finally, we presented a few real IoT examples, which generate fast and streaming data as well as big data. In the next chapter, you will be introduced to several common DL models and the most cutting-edge architectures that have been introduced in recent years, and learn how they can be useful in analyzing IoT streaming and big data.

It is essential to know the basics of different DL models and their different implementation frameworks in order to use them in different IoT applications...

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