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

DL for healthcare

DL models are evolving as the most powerful and effective computing resources for every industry. They are contributing significantly to the industries' value by improving user experience and enabling more informed decision making. Healthcare is one of the key application domains of DL, and DL has been further progressed by the increasing availability of heterogeneous healthcare data.

Unlike many other industries, the health and medical industries are obtaining highly value-added applications in many areas, including research, innovation, and real-world medical environments. Many of these applications are patient-facing (such as early detection and prediction of cancer, and personalized medicine) and others are for improving the user experience in various aspects of healthcare IT. The following diagram highlights some of the application areas of DL in healthcare...