VideoLibrary

Edge AI Anomaly Detection Part 4: Deploy TinyML Model in Arduino to ESP32 | DigiKey

In this episode, we will train a more robust model for anomaly detection and deploy both Mahalanobis Distance and Autodencoder neural network models to our ESP32 via Arduino.

Edge AI Anomaly Detection Part 4: Deploy TinyML Model in Arduino to ESP32 Blog
5/29/2020 5:30:37 PM

Part List

ImageManufacturer Part NumberDescriptionAvailable QuantityPriceView Details
MSA301 TRIPLE AXIS ACCELEROMETER4344MSA301 TRIPLE AXIS ACCELEROMETER0 - ImmediateSee Page for PricingView Details
HUZZAH32 ESP32 FEATHER MALE HDR3591HUZZAH32 ESP32 FEATHER MALE HDR176 - Immediate$18.09View Details

Related Videos

Edge AI Anomaly Detection Part 3: Deploy Machine Learning Models to Raspberry Pi | DigiKey
Edge AI Anomaly Detection Part 3: Deploy Machine Learning Models to Raspberry Pi | DigiKey

Publish Date: 2020-05-29

Edge AI Anomaly Detection Part 1: Data Collection | DigiKey
Edge AI Anomaly Detection Part 1: Data Collection | DigiKey

Publish Date: 2020-04-24

Edge AI Anomaly Detection Part 2: Feature Extraction and Model Training | DigiKey
Edge AI Anomaly Detection Part 2: Feature Extraction and Model Training | DigiKey

Publish Date: 2020-04-24

Related Articles and Blogs

How to Use Cloud Storage on the Atmosphere IoT How to save specific values from Atmosphere IoT to the cloud and retrieve that data.
How to Use the Average Element in the Atmosphere IoT Studio Increasing sensor accuracy is possible by averaging the data points to eliminate extreme points. It’s simple with Atmosphere IoT Studio programming blocks.
How to Use the Pi Weather Shield with Atmosphere IoT Studio The Pi Weather Shield is a perfect tool for measuring the world around your project, on the move or not, with Atmosphere IoT Studio stay updated everywhere.