2 个仓库
Awesome GitHub RepositoriesDetection Algorithm Selection
Mechanisms for switching between different mathematical or ML-based detection strategies at runtime.
Distinct from Anomaly Detection Algorithms: Candidates are specific algorithms (edge detection, cycle detection), not the meta-capability of selecting an algorithm.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Detection Algorithm Selection. Refine with filters or upvote what's useful.
Awesome Detection Algorithm Selection GitHub Repositories
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- francescopace/espectre
francescopace/espectre
6,472在 GitHub 上查看Espectre is an edge machine learning framework and motion detection platform that uses Wi-Fi Channel State Information to identify human presence and movement. It functions as a sensing toolkit for ESP32 microcontrollers, enabling the detection of motion through walls without the use of cameras or wearables. The project distinguishes itself by executing compact neural network classifiers and mathematical detection algorithms directly on the microcontroller. It utilizes a MicroPython runtime to allow for the prototyping and deployment of sensing logic and wireless signal processing algorithms
Enables switching between statistical methods and neural networks to process motion detection data.
Pythoncsidiyesp-32 - chiphuyen/ml-interviews-book
chiphuyen/ml-interviews-book
4,523在 GitHub 上查看This project is a collection of comprehensive guides and reference materials designed for technical interviews, machine learning system design, and professional development. It serves as a technical knowledge base and a career coaching manual, providing structured resources to help candidates navigate the machine learning hiring landscape. The resource distinguishes itself by offering detailed frameworks for comparing industry roles, analyzing company types, and planning long-term career progression. It provides specific guidance on evaluating employer organizational health, identifying resea
Guides algorithm selection for initial prototyping in fraud detection and similar domains.
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