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

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Product Details

Publication date : Jun 27, 2019

Length 308 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781789616132

Category :

Data

Languages :

Python (Intermediate)

Concepts :

Deep Learning

Tools :

TensorFlow (Intermediate)

15 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks

Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks

2. The End-to-End Life Cycle of the IoT

The End-to-End Life Cycle of the IoT

The E2E life cycle of the IoT

IoT application domains

The importance of analytics in IoT

The motivation to use DL in IoT data analytics

The key characteristics and requirements of IoT data

Summary

Reference

3. Deep Learning Architectures for IoT

Deep Learning Architectures for IoT

A soft introduction to ML

ML tasks

Delving into DL

Artificial neural networks

Neural network architectures

DL frameworks and cloud platforms for IoT

Summary

4. Section 2: Hands-On Deep Learning Application Development for IoT

Section 2: Hands-On Deep Learning Application Development for IoT

5. Image Recognition in IoT

Image Recognition in IoT

IoT applications and image recognition

Use case one – image-based automated fault detection

Use case two – image-based smart solid waste separation

Transfer learning for image recognition in IoT

CNNs for image recognition in IoT applications

Collecting data for use case one

Collecting data for use case two

6. Audio/Speech/Voice Recognition in IoT

Audio/Speech/Voice Recognition in IoT

Speech/voice recognition for IoT

Use case one – voice-controlled smart light

Use case two – voice-controlled home access

DL for sound/audio recognition in IoT

CNNs and transfer learning for speech recognition in IoT applications

7. Indoor Localization in IoT

Indoor Localization in IoT

An overview of indoor localization

DL-based indoor localization for IoT

Example – Indoor localization with Wi-Fi fingerprinting

Deployment techniques

Summary

8. Physiological and Psychological State Detection in IoT

Physiological and Psychological State Detection in IoT

IoT-based human physiological and psychological state detection

Use case one – remote progress monitoring of physiotherapy

Use case two — IoT-based smart classroom

Deep learning for human activity and emotion detection in IoT

LSTM, CNNs, and transfer learning for HAR/FER in IoT applications

9. IoT Security

Security attacks in IoT and detections

Use case one: intelligent host intrusion detection in IoT

Use case two: traffic-based intelligent network intrusion detection in IoT

DL for IoT security incident detection

10. Section 3: Advanced Aspects and Analytics in IoT

Section 3: Advanced Aspects and Analytics in IoT

11. Predictive Maintenance for IoT

Predictive Maintenance for IoT

Predictive maintenance for IoT

Example – PM for an aircraft gas turbine engine

DL for predicting RLU

FAQs

Summary

12. Deep Learning in Healthcare IoT

Deep Learning in Healthcare IoT

IoT in healthcare

Use case one – remote management of chronic disease

Use case two – IoT for acne detection and care

13. What's Next - Wrapping Up and Future Directions

What's Next - Wrapping Up and Future Directions

What we have covered in this book?

Deployment challenges of DL solutions in resource-constrained IoT devices

Existing solutions to support DL in resource-constrained IoT devices

Potential future solutions

Summary

References

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About the 3 authors

Aditya Trivedi

See other products by Aditya Trivedi

Mohammad Abdur Razzaque

Dr. Mohammad Abdur Razzaque is a senior lecturer in the School of Computing and Digital Technologies, Teesside University, UK. He has more than 14 years of research and development and teaching experience on distributed systems (Internet of Things, P2P networking, and cloud computing) as well as experience in cybersecurity. He is an expert in end-to-end (sensors-to-cloud) IoT solutions. He offers consultancy in the areas of IoT solutions and the use of machine learning techniques in businesses. He has successfully published more than 65 research papers in these areas. He holds a PhD in distributed systems (P2P wireless sensor networks, mobile ad hoc networks) from the School of Computer Science and Informatics, UCD, Dublin (2008).

See other products by Mohammad Abdur Razzaque

Md. Rezaul Karim

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.

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Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications

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