cars's Stars
avelino/awesome-go
A curated list of awesome Go frameworks, libraries and software
opentofu/opentofu
OpenTofu lets you declaratively manage your cloud infrastructure.
pulumi/pulumi
Pulumi - Infrastructure as Code in any programming language 🚀
GoogleCloudPlatform/terraformer
CLI tool to generate terraform files from existing infrastructure (reverse Terraform). Infrastructure to Code
potatoqualitee/eol-dr
A crowd-sourced guide to help techs help their non-tech spouses / partners / parents / kids when we are at the end-of-life
PhrozenIO/PowerRemoteDesktop
Remote Desktop entirely coded in PowerShell.
vmware-tanzu/community-edition
VMware Tanzu Community Edition is no longer an actively maintained project. Code is available for historical purposes only.
intro-stat-learning/ISLP_labs
Up-to-date version of labs for ISLP
kchristensen/udm-le
Let's Encrypt support for Ubiquiti UniFi OS
rgl/packer-plugin-windows-update
Packer plugin for installing Windows updates
hassio-addons/addon-unifi
UniFi Network Application - Home Assistant Community Add-ons
ansible/ansible-rulebook
fabianishere/udm-kernel
Custom Linux kernels for the UniFi Dream Machine
beele/homebridge-unifi-protect-camera-motion
Camera & Motion sensor support for Unifi Protect cameras in Homekit via Homebridge
hobbyquaker/unifi2mqtt
Connect Ubiquiti UniFi controller to MQTT :satellite:
dim13/unifi
Ubiquiti Unifi Go API
schochastics/football-data
football (soccer) datasets
thib3113/unifi-client
NodeJs client for Unifi products - https://www.ui.com/
stancel/ansible-playbook-scaffolding
This repos holds a skeleton/scaffolding structure for creating Ansible playbooks based on the recommended directory layout
trstruth/rehearsal
Edit orquesta workflow files and visualize the corresponding graph
kalenarndt/udmp-jumbo-frames
Shell script to configure and monitor the jumbo frame configuration on the UDM Pro
ntkme/unifi-systemd-units
:package: Systemd Units for UniFi OS.
ntkme/unifi-systemd
:package: Systemd Service for UniFi OS.
neilalexander/vyatta-yggdrasil
An Yggdrasil package for Ubiquiti EdgeOS and VyOS, allowing Yggdrasil to be used on EdgeRouters
rguske/kubernetes-appliance
A Packer reference to build a VMware PhotonOS based Kubernetes appliance.
marcgarnica13/ml-interpretability-european-football
Understanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
StackStorm-Exchange/stackstorm-excel
excel actions to read and write variables to an excel file
StackStorm-Exchange/stackstorm-vyatta
ammesonb/ubiquiti-config-generator
Dynamically generates configurations for Ubiquiti routers, based on local configuration files with consistency checking prior to deployment
niels-s/unifi-terraform-example
Terraform configuration of the Unifi Controller on Digital Ocean (Nginx, Let's Encrypt, CoreOS)