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  • Data Curation and Feature Scaling

    Data Curation and Feature Scaling

    Run Time: 19 Minutes

    In this IoT Central MicroSession with Edge Impulse, learn how to perform feature normalization and standardization prior to uploading data to Edge Impulse. This process involves calculating the mean and standard deviation of each channel and standardizing the data along a single axis to have a mean of 0 and a standard deviation of 1. The means and standard deviations will need to be recorded for inference during deployment. We use Google Colab to perform this analysis and transformation prior to uploading the data to Edge Impulse.

    Instructor: Shawn Hymel Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: None

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Data Collection with a Custom Sensor

    Data Collection with a Custom Sensor

    Run Time: 25 minutes

    In this IoT Central MicroSession with Edge Impulse, learn how to collect data from custom sensors and store the readings in CSV files for analysis and uploading to Edge Impulse. For seamless efficiency, the Edge Impulse Data Forwarder tool can be used to automatically send data to the Edge Impulse Studio using an internet connection with your embedded system tethered to your computer via USB. By storing data to CSV files, you can then collect samples in the field without an internet connection.

    Instructor: Shawn Hymel, Senior Developer Relations Engineer, Edge Impulse

    Required Hardware: Arduino Nano 33 BLE Sense

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Create Real-Time Object Detection on Low-Power Microcontrollers

    Create Real-Time Object Detection on Low-Power Microcontrollers

    Run Time: 19 Minutes

    In this IoT Central MicroSession with Edge Impulse, learn how to create a real-time object detection system (FOMO) using low-power microcontrollers. You will learn how to collect a high quality object detection dataset to train and deploy a FOMO model to a microcontroller like the Himax WE-I Plus.

    Instructor: Jenny Plunkett, Senior Developer Relations Engineer, Edge Impulse

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Classify Non-Human Voice Audio for Automation and Embedded Machine Learning

    Classify Non-Human Voice Audio for Automation and Embedded Machine Learning

    Run Time: 23 Minutes

    In this IoT Central MicroSession, learn how to create a non-human voice audio classification system to identify when a faucet is leaking. This demo can also be adapted for industrial use cases, such as identifying machine failures with grinding sounds, or even for wildlife conservation purposes, to recognize the vocalizations of various endangered species in the wild.

    Instructor: Jenny Plunkett, Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: Mobile Phone

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Auto ML with EON Tuner

    Auto ML with EON Tuner

    Run Time: 18 Minutes

    In this IoT Central MicroSession with Edge Impulse, learn how to speed-up your machine learning pipeline design using the EON Tuner, Edge Impulse's tool for AutoML and much more. The EON Tuner analyzes your input data, potential signal processing blocks, and neural network architectures, providing an overview of possible model architectures that will fit your chosen device's latency and memory requirements. Louis will walk you through this process and show how to leverage the tool in just 18 minutes.

    Instructor: Louis Moreau, Senior Developer Relations Engineer, Edge Impulse

    Required Hardware: none

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Advanced Sensor Anomaly Detection

    Advanced Sensor Anomaly Detection

    In this IoT Central MicroSession with Edge Impulse, learn how you can easily create a machine learning classifier to detect anomalous sensor readings using only nominal training data for embedded machine learning models directly in the Edge Impulse Studio.

    Instructor: Jenny Plunkett, Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: mobile phone or a microcontroller with accelerometer sensor

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • How to Build Continuous Audio Classification Applications

    How to Build Continuous Audio Classification Applications

    Run Time: 16 Minutes

    In this IoT Central MicroSession with Edge Impulse, quickly learn how to build continuous audio classification applications using Edge Impulse. First see a walkthrough of the theoretical understanding of the continuous classification methods before diving into the demo. Then follow a demo of the data collection and exploration, impulse design, and finally perform live inference on an Arduino Nano 33 BLE Sense, smartphone or anyone one of the boards listed below.

    Instructor: Clinton Odour, Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: Any smartphone and most boards with a microphone or an accelerometer. Examples include: ST B-L475E-IOT01A, Arduino Nano 33 BLE Sense, ESP32 FireBeetle with LIS3DHTR accelerometer, Himax WE-I Plus, Nordic Semiconductor nRF52840 DK, nRF5340 DK or nRF9160 DK, Silicon Labs Thunderboard Sense 2, Sony's Spresense, TI CC1352P LaunchPad, Raspberry Pi 4, Nvidia Jetson and more.

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • How to Classify Motions Using Embedded ML & Accelerometers

    How to Classify Motions Using Embedded ML & Accelerometers

    Run Time: 15 Minutes

    In this IoT Central On-Demand MicroSession, learn how to classify three-axis motion signatures with machine learning using Edge Impulse. You will navigate through the full MLOps pipeline from the data collection to the live inferencing passing by the digital signal processing features extraction and the neural network training.

    Instructor: Louis Moreau, Senior DevRel Engineer, Edge Impulse

    Optional Hardware: Sensor or smartphone with 3-axis accelerometer

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • How to Create a Keyword Spotting System in 20 minutes

    How to Create a Keyword Spotting System in 20 minutes

    Run Time: 27 Minutes

    In this IoT Central MicroSession, learn how to create a keyword spotting system to recognize spoken words. Follow along or use an Arduino board (details below) and classify words using Edge Impulse.

    Instructor: Shawn Hymel, Senior DevRel Engineer, Edge Impulse

    Optional Hardware: Arduino Nano 33 BLE Sense

    Preperation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Inference with Custom Sensors

    Inference with Custom Sensors

    Run Time: 20 Minutes

    In this IoT Central MicroSession, learn how to create a keyword spotting system to recognize spoken words. Follow along or use an Arduino board (details below) and classify words using Edge Impulse.

    Instructor: Shawn Hymel, Senior DevRel Engineer, Edge Impulse

    Optional Hardware: Arduino Nano 33 BLE Sense

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Learn How to Integrate an Object Detection Model in a Custom Application

    Learn How to Integrate an Object Detection Model in a Custom Application

    Run Time: 12 Minutes

    In this IoT Central MicroSession with Edge Impulse, learn how to integrate object detection models with the Linux Python SDK to build fully-customizable applications. In this example, you will see how an object detection model running locally can output a live video stream with the bounding boxes in a web application.

    Instructor: Louis Moreau, Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: Linux x86 or MacOS

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Learn to Build Object Detection Using Transfer Learning

    Learn to Build Object Detection Using Transfer Learning

    Run Time: 15 Minutes

    In this IoT Central MicroSession with Edge Impulse, quickly learn how to build an object detection model using Transfer Learning. You will also learn how to run the live inference using Edge Impulse Linux CLI or MacOS-based hardware. This is the first ion a two part series (coming soon).

    Instructor: Louis Moreau, Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: Linux-based or MacOS-based hardware

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Using Machine Learning for Sensor Fusion

    Using Machine Learning for Sensor Fusion

    Run Time: 22 Minutes

    In this IoT Central MicroSession, learn about use cases for sensor fusion and how it can be accomplished using neural networks. You will walk through how a machine learning model performing sensor fusion can be trained in Edge Impulse and tested on a microcontroller.

    Instructor: Shawn Hymel, Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: Arduino Nano 33 BLE Sense

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Using Image Data Augmentation to Train a Better ML Model

    Using Image Data Augmentation to Train a Better ML Model

    Run Time: 18 Minutes

    In this IoT Central MicroSession with Edge Impulse, learn the process of performing data augmentation on an image dataset, which includes flipping, translating, zooming, rotating, and adding noise. This process generates modified copies of the original data. See how to train a convolutional neural network (CNN) on Edge Impulse with the augmented dataset and demonstrate how it is more accurate than a model trained on the original data.

    Instructor: Shawn Hymel, Senior Developer Relations Engineer, Edge Impulse

    Required Hardware: None

    Preparation:
    Free sign-up at https://studio.edgeimpulse.com/signup
    Code and dataset: https://github.com/ShawnHymel/computer-vision-with-embedded-machine-learning

    Additional Resources:
    A non-augmented version of the project used in the video: studio.edgeimpulse.com/public/36514/latest
    An example of the project with augmented data: studio.edgeimpulse.com/public/36800/latest
  • Using the C++ SDK Library to Perform Inference on Any Device

    Using the C++ SDK Library to Perform Inference on Any Device

    Run Time: 29 Minutes

    In this IoT Central MicroSession with Edge Impulse, see how to use the Edge Impulse C++ SDK library to perform inference on any platform. You will learn how to build a C++ application that includes the necessary library functions to predict class probabilities from a static set of raw features, as well as construct a Makefile to link the libraries together.

    Instructor: Shawn Hymel, Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: Lunix computer, (i.e Raspberry Pi)

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup
  • Data Collection with a Custom Sensor

    Data Collection with a Custom Sensor

    Collect data from custom sensors and store the readings in CSV files for analysis and uploading to Edge Impulse.

  • Using Image Data Augmentation to Train a Better ML Model

    Using Image Data Augmentation to Train a Better ML Model

    Learn the process of performing data augmentation on an image dataset, which includes flipping, translating, zooming, rotating, and adding noise.

  • Advanced Sensor Anomaly Detection

    Advanced Sensor Anomaly Detection

    How to easily create a machine learning classifier to detect anomalous sensor readings using only nominal training data for embedded machine learning models directly in the Edge Impulse Studio.

  • Learn How to Integrate an Object Detection Model in a Custom Application

    Learn How to Integrate an Object Detection Model in a Custom Application

    Learn how to integrate object detection models with the Linux Python SDK to build fully-customizable applications.

  • Data Curation and Feature Scaling

    Data Curation and Feature Scaling

    Learn how to perform feature normalization and standardization prior to uploading data to Edge Impulse. This process involves calculating the mean and standard deviation of each channel and standardizing the data along a single axis to have a mean of 0 and a standard deviation of 1. The means and standard deviations will need to be recorded for inference during deployment. We use Google Colab to perform this analysis and transformation prior to uploading the data to Edge Impulse.

    Instructor: Shawn Hymel Senior Developer Relations Engineer, Edge Impulse

    Optional Hardware: None

    Preparation: Free sign-up at https://studio.edgeimpulse.com/signup

  • Inference with Custom Sensors

    Inference with Custom Sensors

    Learn how to create a keyword spotting system to recognize spoken words. Follow along or use an Arduino board (details below) and classify words using Edge Impulse.

  • Auto ML with EON Tuner

    Auto ML with EON Tuner

    Learn how to speed-up your machine learning pipeline design using the EON Tuner, Edge Impulse's tool for AutoML and much more.

  • Automate your ML Pipeline

    Automate your ML Pipeline

    In this IoT Central MicroSession with Edge Impulse, learn how to create complete automated data pipelines so you can work on your active learning strategies.