VideoLibrary

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

In the third episode, we train a new set of machine learning models and deploy them to a Raspberry Pi. We rely on the Mahalanobis Distance and a neural network to detect anomalies in accelerometer data.

Edge AI Anomaly Detection Part 3: Deploy Machine Learning Models to Raspberry Pi Blog
5/29/2020 5:30:37 PM

Part List

이미지제조업체 부품 번호제품 요약주문 가능 수량가격세부 정보 보기
HUZZAH32 ESP32 FEATHER MALE HDR3591HUZZAH32 ESP32 FEATHER MALE HDR282 - 즉시$32,028.00세부 정보 보기
MSA301 TRIPLE AXIS ACCELEROMETER4344MSA301 TRIPLE AXIS ACCELEROMETER0 - 즉시See Page for Pricing세부 정보 보기

Related Videos

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

게시 날짜: 2020-05-29

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

게시 날짜: 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

게시 날짜: 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.
이용 약관 개인정보 취급 관련 공지사항 쿠키 설정
현지 지원: 경기도 수원시 영통구 창룡대로 256번길 91, 1305호 (우) 16229 Copyright © 1995-2026, DigiKey. All Rights Reserved.
ECIA Member
This site is protected by VikingCloud's Trusted Commerce program
ECIA Member