Implement popular deep learning techniques to make your IoT applications smarter
Key Features
- Understand how deep learning facilitates fast and accurate analytics in IoT
- Build intelligent voice and speech recognition apps in TensorFlow and Chainer
- Analyze IoT data for making automated decisions and efficient predictions
Book Description
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
Who this book is for
If you're an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book.
Table of Contents
- End-to-End Life Cycle of IoT
- Deep Learning Architectures for IoT
- Image Recognition in IoT
- Audio/Speech/Voice Recognition in IoT
- Indoor localization in IoT
- Physiological and Psychological State Detection in IoT
- Security and privacy for IoT
- Predictive Maintenance for IoT
- Deep learning in Healthcare IoT
- What's next: Wrapping Up and Future Directions
Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.
