Hands-On Artificial Intelligence for IoT电子书

Title Page

Copyright and Credits

Hands-On Artificial Intelligence for IoT

Dedication

About Packt

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Packt.com

Contributors

About the author

About the reviewers

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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

Principles and Foundations of IoT and AI

What is IoT 101?

IoT reference model

IoT platforms

IoT verticals

Big data and IoT

Infusion of AI – data science in IoT

Cross-industry standard process for data mining

AI platforms and IoT platforms

Tools used in this book

TensorFlow

Keras

Datasets

The combined cycle power plant dataset

Wine quality dataset

Air quality data

Summary

Data Access and Distributed Processing for IoT

TXT format

Using TXT files in Python

CSV format

Working with CSV files with the csv module

Working with CSV files with the pandas module

Working with CSV files with the NumPy module

XLSX format

Using OpenPyXl for XLSX files

Using pandas with XLSX files

Working with the JSON format

Using JSON files with the JSON module

JSON files with the pandas module

HDF5 format

Using HDF5 with PyTables

Using HDF5 with pandas

Using HDF5 with h5py

SQL data

The SQLite database engine

The MySQL database engine

NoSQL data

HDFS

Using hdfs3 with HDFS

Using PyArrow's filesystem interface for HDFS

Summary

Machine Learning for IoT

ML and IoT

Learning paradigms

Prediction using linear regression

Electrical power output prediction using regression

Logistic regression for classification

Cross-entropy loss function

Classifying wine using logistic regressor

Classification using support vector machines

Maximum margin hyperplane

Kernel trick

Classifying wine using SVM

Naive Bayes

Gaussian Naive Bayes for wine quality

Decision trees

Decision trees in scikit

Decision trees in action

Ensemble learning

Voting classifier

Bagging and pasting

Improving your model – tips and tricks

Feature scaling to resolve uneven data scale

Overfitting

Regularization

Cross-validation

No Free Lunch theorem

Hyperparameter tuning and grid search

Summary

Deep Learning for IoT

Deep learning 101

Deep learning—why now?

Artificial neuron

Modelling single neuron in TensorFlow

Multilayered perceptrons for regression and classification

The backpropagation algorithm

Energy output prediction using MLPs in TensorFlow

Wine quality classification using MLPs in TensorFlow

Convolutional neural networks

Different layers of CNN

The convolution layer

Pooling layer

Some popular CNN model

LeNet to recognize handwritten digits

Recurrent neural networks

Long short-term memory

Gated recurrent unit

Autoencoders

Denoising autoencoders

Variational autoencoders

Summary

Genetic Algorithms for IoT

Optimization

Deterministic and analytic methods

Gradient descent method

Newton-Raphson method

Natural optimization methods

Simulated annealing

Particle Swarm Optimization

Genetic algorithms

Introduction to genetic algorithms

The genetic algorithm

Crossover

Mutation

Pros and cons

Advantages

Disadvantages

Coding genetic algorithms using Distributed Evolutionary Algorithms in Python

Guess the word

Genetic algorithm for CNN architecture

Genetic algorithm for LSTM optimization

Summary

Reinforcement Learning for IoT

Introduction

RL terminology

Deep reinforcement learning

Some successful applications

Simulated environments

OpenAI gym

Q-learning

Taxi drop-off using Q-tables

Q-Network

Taxi drop-off using Q-Network

DQN to play an Atari game

Double DQN

Dueling DQN

Policy gradients

Why policy gradients?

Pong using policy gradients

The actor-critic algorithm

Summary

Generative Models for IoT

Introduction

Generating images using VAEs

VAEs in TensorFlow

GANs

Implementing a vanilla GAN in TensorFlow

Deep Convolutional GANs

Variants of GAN and its cool applications

Cycle GAN

Applications of GANs

Summary

Distributed AI for IoT

Introduction

Spark components

Apache MLlib

Regression in MLlib

Classification in MLlib

Transfer learning using SparkDL

Introducing H2O.ai

H2O AutoML

Regression in H2O

Classification in H20

Summary

Personal and Home IoT

Personal IoT

SuperShoes by MIT

Continuous glucose monitoring

Hypoglycemia prediction using CGM data

Heart monitor

Digital assistants

IoT and smart homes

Human activity recognition

HAR using wearable sensors

HAR from videos

Smart lighting

Home surveillance

Summary

AI for the Industrial IoT

Introduction to AI-powered industrial IoT

Some interesting use cases

Predictive maintenance using AI

Predictive maintenance using Long Short-Term Memory

Predictive maintenance advantages and disadvantages

Electrical load forecasting in industry

STLF using LSTM

Summary

AI for Smart Cities IoT

Why do we need smart cities?

Components of a smart city

Smart traffic management

Smart parking

Smart waste management

Smart policing

Smart lighting

Smart governance

Adapting IoT for smart cities and the necessary steps

Cities with open data

Atlanta city Metropolitan Atlanta Rapid Transit Authority data

Chicago Array of Things data

Detecting crime using San Francisco crime data

Challenges and benefits

Summary

Combining It All Together

Processing different types of data

Time series modeling

Preprocessing textual data

Data augmentation for images

Handling videos files

Audio files as input data

Computing in the cloud

AWS

Google Cloud Platform

Microsoft Azure

Summary

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