Open solution to the Home Credit Default Risk challenge 🏡
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Updated
Jun 22, 2022 - Python
Open solution to the Home Credit Default Risk challenge 🏡
Collective Knowledge workflow for Caffe to automate installation across diverse platforms and to collaboratively evaluate and optimize Caffe-based workloads across diverse hardware, software and data sets (compilers, libraries, tools, models, inputs):
Open solution to the Google AI Object Detection Challenge 🍁
Open solution to the Santander Value Prediction Challenge 🐠
awesome open list of pointers about open science for software and computational science
Network Performance Framework: easy-to-use experiment manager with automated testing, result collection, and graphing
Spam Filtering Techniques for Short Message Service
Empirical Software Engineering journal (EMSE) open science and reproducible research initiative
This repository is for easybuild open source contributions without restrictions - add your username under the /users directory
A library for creating and curating reproducible pipelines for scientific and industrial machine learning
📊 Reproducible Benchmark for Everyone
HESML Java software library of ontology-based semantic similarity measures and information content models
Architecture and overarching documentation for o2r microservices
Python code to reproduce our article "Toward faultless content-based playlists generation for instrumentals"
Tool demonstrating building credit risk models
Crowdsourcing video experiments (such as collaborative benchmarking and optimization of DNN algorithms) using Collective Knowledge Framework across diverse Android devices provided by volunteers. Results are continuously aggregated in the open repository:
Framework for Reproducible ExperimenTs
Extra resources in the Collective Knowledge Format for ARM's Workload Automation Framework:
A logical and reasonably simple project structure for developing quantitative trading work like strategies and research works.
Experimental artefacts to reproduce our results published at SoCC 2017
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