Nick Freeman's starred repositories
openai-cookbook
Examples and guides for using the OpenAI API
howtheytest
A collection of public resources about how software companies test their software
OpenCopilot
🤖 🔥 Language-to-actions engine
pdf2htmlEX
Convert PDF to HTML without losing text or format.
elasticsearch-rails
Elasticsearch integrations for ActiveModel/Record and Ruby on Rails
SmartThingsPublic
SmartThings open-source DeviceType Handlers and SmartApps code
the-ultimate-guide-to-ruby-timeouts
Timeouts for popular Ruby gems
openapi-to-graphql
Translate APIs described by OpenAPI Specifications (OAS) into GraphQL
testlink-code
TestLink Open Source Test & Requirement Management System
gensim-data
Data repository for pretrained NLP models and NLP corpora.
python-markdownify
Convert HTML to Markdown
GPTSecurity
塑造未来的安全领域智能革命
semantic-python-overview
(subjective) overview of projects which are related both to python and semantic technologies (RDF, OWL, Reasoning, ...)
syn-rep-learn
Learning from synthetic data - code and models
AI-Powered-Video-Tutorial-Generator
Create AI-Generated Video Tutorials with Character Animation and Slides!
vyper-greatest-hits
A collection of Vyper code across various topics and concepts
InformationSecurityOntology
Ontology of the area of Information Security, formalized in the OWL language
Network_Learning_approaches_to_study_World_Happiness
The United Nations in its 2011 resolution declared the pursuit of happiness a fundamental human goal and proposed public and economic policies centered around happiness. In this paper we used 2 types of computational strategies viz. \textit{Predictive Modelling} and \textit{Bayesian Networks(BNs)} to model the processed historical happiness index data of 156 nations published by UN since 2012. We attacked the problem of prediction using GRNNs and show that it out performs other state of the art predictive models. To understand causal links amongst key features that have been proven to have a significant impact on world happiness, we first used a manual discretization scheme to discretize continuous variables into 3 levels viz. \textit{Low, Medium} and \textit{High}. A consensus World Happiness BN structure was then fixed after amalgamating information by learning 10000 different BNs using bootstrapping. Lastly, exact inference through conditional probability queries was used on this BN to unravel interesting relationships among the important features affecting happiness which would be useful in policy making.