Book Image

Hands-On Deep Learning for IoT

By : Dr. Mohammad Abdur Razzaque, Md. Rezaul Karim
Book Image

Hands-On Deep Learning for IoT

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)
Free Chapter
1
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

References

  • Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions), at https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/.
  • Real-Time Detection of Application-Layer DDoS Attack Using Time Series Analysis, T. Ni, X. Gu, H. Wang, and Y. Li, Journal of Control Science and Engineering, vol. 2013, pp. 1–6, 2013.
  • DDoS in the IoT: Mirai and Other Botnets, C. Kolias, G. Kambourakis, A. Stavrou, and J. Voas, IEEE Computer, vol. 50, no. 7, pp. 80–84, 2017.
  • 2016 Dyn cyberattack, at https://en.wikipedia.org/wiki/2016_Dyn_cyberattack.
  • A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder, Tao X., Kong D., Wei Y., and Wang Y. (2016), Information, 7(2), 20.
  • An Effective Intrusion Detection Classifier Using Long Short-Term Memory with Gradient Descent Optimization, Kim J....