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Hands-On Deep Learning for IoT

Hands-On Deep Learning for IoT

By : Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim
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Hands-On Deep Learning for IoT

Hands-On Deep Learning for IoT

4 (1)
By: Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim

Overview of this book

Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making.
Table of Contents (15 chapters)
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Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
4
Section 2: Hands-On Deep Learning Application Development for IoT
10
Section 3: Advanced Aspects and Analytics in IoT

What's Next - Wrapping Up and Future Directions

The use of IoT will be come omnipresent in our life through various applications, including connected healthcare, smart home, and smart city. Although IoT applications are exciting, there are significant scientific and technological challenges to be overcome before they can be fully realized. One of the key technical challenges for IoT is the design and development of Deep Learning (DL) models that work well in resource-constrained IoT devices or edge/fog computing devices, and those that meet the real-time response requirements. In this final chapter, we shall first present a summary of the earlier chapters, and then use examples to discuss the main challenges faced by the existing DL techniques in their development and implementation of resource-constrained and embedded IoT environments. Finally, we will summarize a few existing...

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