GitHub

CHANGES.md

Fix cpu_count(only_physical_cores=True) to always take the number of physical cores into account, even when the usable CPU count is already restricted by CPU affinity, Cgroup, or LOKY_MAX_CPU_COUNT.
GitHub

BharatLens ML: Indian District Socioeconomic Intelligence & Trend Forecasting Platform

BharatLens ML is an empirical district intelligence platform engineered to bridge the analytical gap between raw public survey releases and actionable policy foresight across India's administrative ...
Frontiers

Development of an efficient deep learning model for arrhythmia detection from consumer smartwatches

Leveraging Artificial Intelligence and Real World Data for Novel Insights in Healthcare.
blog.csdn

CentOS 7.9上构建Python机器学习流水线：从环境搭建到落地实践

文章浏览阅读8次。机器学习项目落地时，存量服务器环境往往是不可忽视的约束条件。CentOS 7.9这类老系统虽然内核和glibc版本偏低，但通过合理的Python环境构建与依赖版本管理，依然可以稳定运行完整的Scikit-learn训练流程。从源码编译Python 3.8到创建虚拟环境，再到用Pipeline和ColumnTransformer统一特征 ...
blog.csdn

深度学习+Python入侵检测实战：数据、模型、阈值与部署避坑

python 3.8之后的环境配置也稳定许多，常见做法是创建一个干净虚拟环境，装numpy、pandas、scikit-learn、torch、scapy或dpkt，然后再开始写数据脚本。 网上那些python教程和源码合集里，流量分析脚本并不少，真正缺的是把脚本串成一条完整训练管线的能力。
huggingface

Korean Accounting Standard Matcher

코드 실행 전에 포함된 소스를 확인하세요. Tested with Python 3.11 on CPU. Download about 1.29 GB of Laya weights and 76.4 MB of logistic inference artifacts, plus additional memory for loading. This repository is ...