Although the IoT application use cases, presented herein with their DL-based implementations, demonstrate the potential of DL in IoT, there are still some open research challenges in many directions. In particular, research and development support are needed in many areas. A few of the key areas of research and development are dataset preprocessing, secure, and privacy-aware DL, aspects of handling big data, and resource-efficient training and learning. In the following sections, we briefly present these remaining challenges from a machine learning perspective, as well as from the perspective of IoT devices, edge/fog computing, and the cloud.
Hands-On Deep Learning for IoT
By :
Hands-On Deep Learning for IoT
By:
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.
Related Content you might be interested in
Current Title:
Hands-On Deep Learning for IoT
Table of Contents (15 chapters)
Preface
Free Chapter
Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
The End-to-End Life Cycle of the IoT
Deep Learning Architectures for IoT
Section 2: Hands-On Deep Learning Application Development for IoT
Image Recognition in IoT
Audio/Speech/Voice Recognition in IoT
Indoor Localization in IoT
Physiological and Psychological State Detection in IoT
IoT Security
Section 3: Advanced Aspects and Analytics in IoT
Predictive Maintenance for IoT
Deep Learning in Healthcare IoT
What's Next - Wrapping Up and Future Directions
Other Books You May Enjoy
Customer Reviews