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Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT data

Key Features

  • Discover quick solutions to common problems that you'll face while building smart IoT applications
  • Implement advanced techniques such as computer vision, NLP, and embedded machine learning
  • Build, maintain, and deploy machine learning systems to extract key insights from IoT data

Book Description

Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications.

Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease.

By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.

What you will learn

  • Explore various AI techniques to build smart IoT solutions from scratch
  • Use machine learning and deep learning techniques to build smart voice recognition and facial detection systems
  • Gain insights into IoT data using algorithms and implement them in projects
  • Perform anomaly detection for time series data and other types of IoT data
  • Implement embedded systems learning techniques for machine learning on small devices
  • Apply pre-trained machine learning models to an edge device
  • Deploy machine learning models to web apps and mobile using TensorFlow.js and Java

Who this book is for

If you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.

Table of Contents

  1. Title Page
  2. Copyright and Credits
    1. Artificial Intelligence for IoT Cookbook
  3. Contributors
    1. About the author
    2. About the reviewer
  4. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Download the color images
    6. Conventions used
    7. Sections
    8. Getting ready
    9. How to do it…
    10. How it works…
    11. There's more…
    12. See also
    13. Get in touch
    14. Reviews
  5. Setting Up the IoT and AI Environment
    1. Choosing a device
    2. Dev kits  
    3. Manifold 2-C with NVIDIA TX2
    4. The i.MX series
    5. LattePanda 
    6. Raspberry Pi Class
    7. Arduino
    8. ESP8266 
    9. Setting up Databricks
    10. Storing data
    11. Parquet
    12. Avro
    13. Delta Lake
    14. Setting up IoT Hub
    15. Getting ready
    16. How to do it...
    17. How it works...
    18. Setting up an IoT Edge device
    19. Getting ready
    20. How to do it...
    21. Configuring an IoT Edge device (cloud side)
    22. Configuring an IoT Edge device (device side)
    23. How it works...
    24. Deploying ML modules to Edge devices
    25. Getting ready
    26. How to do it...
    27. How it works...
    28. There's more...
    29. Setting up Kafka
    30. Getting ready
    31. How to do it...
    32. How it works...
    33. There's more...
    34. Installing ML libraries on Databricks
    35. Getting ready
    36. How to do it...
    37. Importing TensorFlow
    38. Installing PyTorch
    39. Installing GraphX and GraphFrames
    40. How it works...
  6. Handling Data
    1. Storing data for analysis using Delta Lake
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. Data collection design
    6. Getting ready
    7. How to do it...
    8. Variance
    9. Z-Spikes
    10. Min/max
    11. Windowing
    12. Getting ready
    13. How to do it...
    14. Tumbling
    15. Hopping
    16. Sliding
    17. How it works...
    18. Exploratory factor analysis
    19. Getting ready
    20. How to do it...
    21. Visual exploration
    22. Chart types
    23. Redundant sensors
    24. Sample co-variance and correlation
    25. How it works...
    26. There's more...
    27. Implementing analytic queries in Mongo/hot path storage
    28. Getting ready
    29. How to do it...
    30. How it works...
    31. Ingesting IoT data into Spark
    32. Getting ready
    33. How to do it...
    34. How it works...
  7. Machine Learning for IoT
    1. Analyzing chemical sensors with anomaly detection
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. There's more...
    6. Logistic regression with the IoMT 
    7. Getting ready
    8. How to do it...
    9. How it works...
    10. There's more...
    11. Classifying chemical sensors with decision trees
    12. How to do it...
    13. How it works...
    14. There's more...
    15. Simple predictive maintenance with XGBoost
    16. Getting ready
    17. How to do it...
    18. How it works...
    19. Detecting unsafe drivers
    20. Getting ready
    21. How to do it...
    22. How it works...
    23. There's more...
    24. Face detection on constrained devices
    25. Getting ready
    26. How to do it...
    27. How it works...
  8. Deep Learning for Predictive Maintenance
    1. Enhancing data using feature engineering
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. There's more...
    6. Using keras for fall detection
    7. Getting ready
    8. How to do it...
    9. How it works...
    10. There's more...
    11. Implementing LSTM to predict device failure
    12. Getting ready
    13. How to do it...
    14. How it works...
    15. Deploying models to web services
    16. Getting ready
    17. How to do it...
    18. How it works...
    19. There's more...
  9. Anomaly Detection
    1. Using Z-Spikes on a Raspberry Pi and Sense HAT
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. Using autoencoders to detect anomalies in labeled data
    6. Getting ready
    7. How to do it...
    8. How it works...
    9. There's more...
    10. Using isolated forest for unlabeled datasets
    11. Getting ready
    12. How to do it...
    13. How it works...
    14. There's more...
    15. Detecting time series anomalies with Luminol
    16. Getting ready
    17. How to do it...
    18. How it works...
    19. There's more...
    20. Detecting seasonality-adjusted anomalies
    21. Getting ready
    22. How to do it...
    23. How it works...
    24. Detecting spikes with streaming analytics
    25. Getting ready
    26. How to do it...
    27. How it works...
    28. Detecting anomalies on the edge
    29. Getting ready
    30. How to do it...
    31. How it works...
  10. Computer Vision
    1. Connecting cameras through OpenCV
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. There's more...
    6. Using Microsoft's custom vision to train and label your images
    7. Getting ready
    8. How to do it...
    9. How it works...
    10. Detecting faces with deep neural nets and Caffe
    11. Getting ready
    12. How to do it...
    13. How it works...
    14. Detecting objects using YOLO on Raspberry Pi 4
    15. Getting ready
    16. How to do it...
    17. How it works...
    18. Detecting objects using GPUs on NVIDIA Jetson Nano
    19. Getting ready
    20. How to do it...
    21. How it works...
    22. There's more...
    23. Training vision with PyTorch on GPUs
    24. Getting ready
    25. How to do it...
    26. How it works...
    27. There's more...
  11. NLP and Bots for Self-Ordering Kiosks
    1. Wake word detection
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. There's more...
    6. Speech-to-text using the Microsoft Speech API
    7. Getting ready
    8. How to do it...
    9. How it works...
    10. Getting started with LUIS
    11. Getting ready
    12. How to do it...
    13. How it works...
    14. There's more...
    15. Implementing smart bots
    16. Getting ready
    17. How to do it...
    18. How it works...
    19. There's more...
    20. Creating a custom voice
    21. Getting ready
    22. How to do it...
    23. How it works...
    24. Enhancing bots with QnA Maker
    25. Getting ready
    26. How to do it...
    27. How it works...
    28. There's more...
  12. Optimizing with Microcontrollers and Pipelines
    1. Introduction to ESP32 with IoT 
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. There's more...
    6. Implementing an ESP32 environment monitor
    7. Getting ready
    8. How to do it...
    9. How it works...
    10. There's more...
    11. Optimizing hyperparameters
    12. Getting ready
    13. How to do it...
    14. How it works...
    15. Dealing with BOM changes
    16. Getting ready
    17. How to do it...
    18. How it works...
    19. There's more...
    20. Building machine learning pipelines with sklearn
    21. Getting ready
    22. How to do it...
    23. How it works...
    24. There's more...
    25. Streaming machine learning with Spark and Kafka
    26. Getting ready
    27. How to do it...
    28. How it works...
    29. There's more...
    30. Enriching data using Kafka's KStreams and KTables
    31. Getting ready
    32. How to do it...
    33. How it works...
    34. There's more...
  13. Deploying to the Edge
    1. OTA updating MCUs
    2. Getting ready
    3. How to do it...
    4. How it works...
    5. There's more...
    6. Deploying modules with IoT Edge
    7. Getting ready
    8. Setting up our Raspberry Pi
    9. Coding setup
    10. How to do it...
    11. How it works...
    12. There's more...
    13. Offloading to the web with TensorFlow.js
    14. Getting ready
    15. How to do it...
    16. How it works...
    17. There's more...
    18. Deploying mobile models
    19. Getting ready
    20. How to do it...
    21. How it works...
    22. Maintaining your fleet with device twins
    23. Getting ready
    24. How to do it...
    25. How it works...
    26. There's more...
    27. Enabling distributed ML with fog computing
    28. Getting ready
    29. How to do it...
    30. How it works...
    31. There's more...
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