Watch a 37-minute technical presentation from the On Device Learning Forum where SENAI Technology Specialist Eduardo Dos Santos Pereira explores implementing TinyML for anomaly detection on resource-constrained devices. Learn about the challenges of deploying artificial intelligence on tiny devices, techniques for detecting anomalies in time series data using random forest classification, and the application of extreme values theory. Discover practical experimental results and insights into fog and edge computing implementations for anomaly detection systems.
Online Learning TinyML for Anomaly Detection Based on Extreme Values Theory
EDGE AI FOUNDATION via YouTube
Overview
Syllabus
Intro
Artificial for Tiny Devices
Challenges
Anomaly in Time Series
Random Forest for Anomaly Classification
Extreme Values Theory
Extreme value Theory
Experimental Results
Fog and Edge Computing
Taught by
EDGE AI FOUNDATION
