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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

Collecting data for use case two

As is the case with use case one, we can collect data through digital cameras or use an existing open source or a combination of both. We are using an existing and open source dataset for the implementation of the sorting algorithm. The dataset was collected from urban environments of the USA . As solid waste types may vary by country, it is better to update the dataset based on the country the use case will be used for. The dataset consists of six types of solid wastes: glass, paper, cardboard, plastic, metal, and trash. The dataset consists of 2,527 images, and they were annotated and organized into the following folders, as shown in the following screenshot:

Data exploration of use case two

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