English | 2019 | ISBN: 1789616132 | 308 Pages | EPUB | 40 MB
Implement popular deep learning techniques to make your IoT applications smarter
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.
What you will learn
- Get acquainted with different neural network architectures and their suitability in IoT
- Understand how deep learning can improve the predictive power in your IoT solutions
- Capture and process streaming data for predictive maintenance
- Select optimal frameworks for image recognition and indoor localization
- Analyze voice data for speech recognition in IoT applications
- Develop deep learning-based IoT solutions for healthcare
- Enhance security in your IoT solutions
- Visualize analyzed data to uncover insights and perform accurate predictions