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Hands-On Deep Learning for IoT电子书

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作       者:Abdur Razzaque PhD,Karim Md. Rezaul

出  版  社:Packt Publishing

出版时间:2019-06-27

字       数:32.1万

所属分类: 进口书 > 外文原版书 > 小说

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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 1.End-to-End Life Cycle of IoT 2.Deep Learning Architectures for IoT 3.Image Recognition in IoT 4.Audio/Speech/Voice Recognition in IoT 5.Indoor localization in IoT 6.Physiological and Psychological State Detection in IoT 7.Security and privacy for IoT 8.Predictive Maintenance for IoT 9.Deep learning in Healthcare IoT 10.What’s next: Wrapping Up and Future Directions
目录展开

Title Page

Copyright and Credits

Hands-On Deep Learning for IoT

About Packt

Why subscribe?

Contributors

About the authors

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

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

The End-to-End Life Cycle of the IoT

The E2E life cycle of the IoT

The three-layer E2E IoT life cycle

The five-layer IoT E2E life cycle

IoT system architectures

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

Real-life examples of fast and streaming IoT data

Real-life examples of IoT big data

Summary

Reference

Deep Learning Architectures for IoT

A soft introduction to ML

Working principle of a learning algorithm

General ML rule of thumb

General issues in ML models

ML tasks

Supervised learning

Unsupervised learning

Reinforcement learning

Learning types with applications

Delving into DL

How did DL take ML to the next level?

Artificial neural networks

ANN and the human brain

A brief history of ANNs

How does an ANN learn?

Training a neural network

Weight and bias initialization

Activation functions

Neural network architectures

Deep neural networks

Autoencoders

Convolutional neural networks

Recurrent neural networks

Emergent architectures

Residual neural networks

Generative adversarial networks

Capsule networks

Neural networks for clustering analysis

DL frameworks and cloud platforms for IoT

Summary

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

Image Recognition in IoT

IoT applications and image recognition

Use case one – image-based automated fault detection

Implementing use case one

Use case two – image-based smart solid waste separation

Implementing use case two

Transfer learning for image recognition in IoT

CNNs for image recognition in IoT applications

Collecting data for use case one

Exploring the dataset from use case one

Collecting data for use case two

Data exploration of use case two

Data pre-processing

Models training

Evaluating models

Model performance (use case one)

Model performance (use case two)

Summary

References

Audio/Speech/Voice Recognition in IoT

Speech/voice recognition for IoT

Use case one – voice-controlled smart light

Implementing use case one

Use case two – voice-controlled home access

Implementing use case two

DL for sound/audio recognition in IoT

ASR system model

Features extraction in ASR

DL models for ASR

CNNs and transfer learning for speech recognition in IoT applications

Collecting data

Exploring data

Data preprocessing

Models training

Evaluating models

Model performance (use case 1)

Model performance (use case 2)

Summary

References

Indoor Localization in IoT

An overview of indoor localization

Techniques for indoor localization

Fingerprinting

DL-based indoor localization for IoT

K-nearest neighbor (k-NN) classifier

AE classifier

Example – Indoor localization with Wi-Fi fingerprinting

Describing the dataset

Network construction

Implementation

Exploratory analysis

Preparing training and test sets

Creating an AE

Creating an AE classifier

Saving the trained model

Evaluating the model

Deployment techniques

Summary

Physiological and Psychological State Detection in IoT

IoT-based human physiological and psychological state detection

Use case one – remote progress monitoring of physiotherapy

Implementation of use case one

Use case two — IoT-based smart classroom

Implementation of use case two

Deep learning for human activity and emotion detection in IoT

Automatic human activity recognition system

Automated human emotion detection system

Deep learning models for HAR and emotion detection

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

Data collection

Data exploration

Data preprocessing

Model training

Use case one

Use case two

Model evaluation

Model performance (use case one)

Model performance (use case two)

Summary

References

IoT Security

Security attacks in IoT and detections

Anomaly detection and IoT security

Use case one: intelligent host intrusion detection in IoT

Implementation of use case one

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

Implementation of use case two

DL for IoT security incident detection

DNN, autoencoder, and LSTM in IoT security incidents detection

Data collection

CPU utilisation data

KDD cup 1999 IDS dataset

Data exploration

Data preprocessing

Model training

Use case one

Use case two

Model evaluation

Model performance (use case one)

Model performance (use case two)

Summary

References

Section 3: Advanced Aspects and Analytics in IoT

Predictive Maintenance for IoT

Predictive maintenance for IoT

Collecting IoT data in an industrial setting

ML techniques for predictive maintenance

Example – PM for an aircraft gas turbine engine

Describing the dataset

Exploratory analysis

Inspecting failure modes

Prediction challenges

DL for predicting RLU

Calculating cut-off times

Deep feature synthesis

ML baselines

Making predictions

Improving MAE with LSTM

Unsupervised deep feature synthesis

FAQs

Summary

Deep Learning in Healthcare IoT

IoT in healthcare

Use case one – remote management of chronic disease

Implementation of use case one

Use case two – IoT for acne detection and care

Implementation of use case two

DL for healthcare

CNN and LSTM in healthcare applications

Data collection

Use case one

Use case two

Data exploration

The ECG dataset

The acne dataset

Data preprocessing

Model training

Use case one

Use case two

Model evaluations

Model performance (use case one)

Model performance (use case two)

Summary

References

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

Machine learning/DL perspectives

DL limitations

IoT devices, edge/fog computing, and cloud perspective

Existing solutions to support DL in resource-constrained IoT devices

Potential future solutions

Summary

References

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