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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 FREE CHAPTER
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

LSTM, CNNs, and transfer learning for HAR/FER in IoT applications

LSTM is the widely used DL model for HAR—including in IoT-based HAR—because its memory capacity can deal better with time series data (such as HAR data) than other models, including CNN. The LSTM implementation of HAR can support transfer learning and is suitable for resource-constrained IoT devices. Generally, FER relies on image processing, and the CNN is the best model for image processing. Therefore, we implement use case two (FER) using a CNN model. In Chapter 3, Image Recognition in IoT, we presented an overview of two popular implementations of the CNN (such as incentive V3 and Mobilenets) and their corresponding transfer learning. In the following paragraphs, we briefly present an overview of the baseline LSTM.

LSTM is an extension of RNNs. Many variants of LSTM are proposed, and they follow...

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