book

Artificial Intelligence for IoT Cookbook

Name: Artificial Intelligence for IoT Cookbook
Author: Michael Roshak
ISBN: 9781838981983

by Michael Roshak

March 2021

Intermediate to advanced

260 pages

English

Packt Publishing

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Title Page
Copyright and Credits
Artificial Intelligence for IoT Cookbook
Contributors
About the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedSectionsGetting readyHow to do it…How it works…There's more…See alsoGet in touchReviews
Setting Up the IoT and AI Environment
Choosing a deviceDev kits Manifold 2-C with NVIDIA TX2The i.MX seriesLattePanda Raspberry Pi ClassArduinoESP8266 Setting up DatabricksStoring dataParquetAvroDelta LakeSetting up IoT HubGetting readyHow to do it...How it works...Setting up an IoT Edge deviceGetting readyHow to do it...Configuring an IoT Edge device (cloud side)Configuring an IoT Edge device (device side)How it works...Deploying ML modules to Edge devicesGetting readyHow to do it...How it works...There's more...Setting up KafkaGetting readyHow to do it...How it works...There's more...Installing ML libraries on DatabricksGetting readyHow to do it...Importing TensorFlowInstalling PyTorchInstalling GraphX and GraphFramesHow it works...
Handling Data
Storing data for analysis using Delta LakeGetting readyHow to do it...How it works...Data collection designGetting readyHow to do it...VarianceZ-SpikesMin/maxWindowingGetting readyHow to do it...TumblingHoppingSlidingHow it works...Exploratory factor analysisGetting readyHow to do it...Visual explorationChart typesRedundant sensorsSample co-variance and correlationHow it works...There's more...Implementing analytic queries in Mongo/hot path storageGetting readyHow to do it...How it works...Ingesting IoT data into SparkGetting readyHow to do it...How it works...
Machine Learning for IoT
Analyzing chemical sensors with anomaly detectionGetting readyHow to do it...How it works...There's more...Logistic regression with the IoMT Getting readyHow to do it...How it works...There's more...Classifying chemical sensors with decision treesHow to do it...How it works...There's more...Simple predictive maintenance with XGBoostGetting readyHow to do it...How it works...Detecting unsafe driversGetting readyHow to do it...How it works...There's more...Face detection on constrained devicesGetting readyHow to do it...How it works...
Deep Learning for Predictive Maintenance
Enhancing data using feature engineeringGetting readyHow to do it...How it works...There's more...Using keras for fall detectionGetting readyHow to do it...How it works...There's more...Implementing LSTM to predict device failureGetting readyHow to do it...How it works...Deploying models to web servicesGetting readyHow to do it...How it works...There's more...
Anomaly Detection
Using Z-Spikes on a Raspberry Pi and Sense HATGetting readyHow to do it...How it works...Using autoencoders to detect anomalies in labeled dataGetting readyHow to do it...How it works...There's more...Using isolated forest for unlabeled datasetsGetting readyHow to do it...How it works...There's more...Detecting time series anomalies with LuminolGetting readyHow to do it...How it works...There's more...Detecting seasonality-adjusted anomaliesGetting readyHow to do it...How it works...Detecting spikes with streaming analyticsGetting readyHow to do it...How it works...Detecting anomalies on the edgeGetting readyHow to do it...How it works...
Computer Vision
Connecting cameras through OpenCVGetting readyHow to do it...How it works...There's more...Using Microsoft's custom vision to train and label your imagesGetting readyHow to do it...How it works...Detecting faces with deep neural nets and CaffeGetting readyHow to do it...How it works...Detecting objects using YOLO on Raspberry Pi 4Getting readyHow to do it...How it works...Detecting objects using GPUs on NVIDIA Jetson NanoGetting readyHow to do it...How it works...There's more...Training vision with PyTorch on GPUsGetting readyHow to do it...How it works...There's more...

NLP and Bots for Self-Ordering Kiosks
Wake word detectionGetting readyHow to do it...How it works...There's more...Speech-to-text using the Microsoft Speech APIGetting readyHow to do it...How it works...Getting started with LUISGetting readyHow to do it...How it works...There's more...Implementing smart botsGetting readyHow to do it...How it works...There's more...Creating a custom voiceGetting readyHow to do it...How it works...Enhancing bots with QnA MakerGetting readyHow to do it...How it works...There's more...
Optimizing with Microcontrollers and Pipelines
Introduction to ESP32 with IoT Getting readyHow to do it...How it works...There's more...Implementing an ESP32 environment monitorGetting readyHow to do it...How it works...There's more...Optimizing hyperparametersGetting readyHow to do it...How it works...Dealing with BOM changesGetting readyHow to do it...How it works...There's more...Building machine learning pipelines with sklearnGetting readyHow to do it...How it works...There's more...Streaming machine learning with Spark and KafkaGetting readyHow to do it...How it works...There's more...Enriching data using Kafka's KStreams and KTablesGetting readyHow to do it...How it works...There's more...
Deploying to the Edge
OTA updating MCUsGetting readyHow to do it...How it works...There's more...Deploying modules with IoT EdgeGetting readySetting up our Raspberry PiCoding setupHow to do it...How it works...There's more...Offloading to the web with TensorFlow.jsGetting readyHow to do it...How it works...There's more...Deploying mobile modelsGetting readyHow to do it...How it works...Maintaining your fleet with device twinsGetting readyHow to do it...How it works...There's more...Enabling distributed ML with fog computingGetting readyHow to do it...How it works...There's more...
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Content preview from Artificial Intelligence for IoT Cookbook

Machine Learning for IoT

Machine learning has dramatically altered what manufacturers are able to do with IoT. Today, there are numerous industries that have specific IoT needs. For example, the internet of medical things (IoMT) has devices such as outpatient heart monitors that can be worn at home. These devices often require large amounts of data to be sent over the network or large compute capacity on the edge to process heart-related events. Another example is agricultural IoT (AIoT) devices that are often placed in locations where there is no Wi-Fi or cellular network. Prescriptions or models are pushed down to these semi-connected devices. Many of these devices require that decisions be made on the edge. When connectivity is finally ...

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Publisher Resources

ISBN: 9781838981983

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Artificial Intelligence for IoT Cookbook

by Michael Roshak

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.