gsanou Goto Github PK

awesome-explainable-graph-reasoning

A collection of research papers and software related to explainability in graph machine learning.

awesome-knowledge-graph

A collection of research on knowledge graphs

awesome-knowledge-graph-1

A curated list of Knowledge Graph related learning materials, databases, tools and other resources

awesome-knowledge-management

A curated list of amazingly awesome articles, people, applications, software libraries and projects related to the knowledge management space

awesome-langchain

😎 Awesome list of tools and projects with the awesome LangChain framework

awesome-math

A curated list of awesome mathematics resources

awesome-network-embedding

A curated list of network embedding techniques.

awesome-neuro-symbolic-ai

A curated list of awesome Neuro-Symbolic AI software frameworks

awesome-nosql-guides

:computer: Curated list of awesome resources and links about using NoSQL databases

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-python-data-science

Probably the best curated list of data science software in Python.

awesome-semantic-web

A curated list of various semantic web and linked data resources.

aws-dbs-refarch-graph

Reference Architectures for Graph Databases on AWS

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

bio-kge-apps

Biological applications of knowledge graph embedding methods

course-knowledge-graphs

Test data and example source code for the Knowledge Graphs lecture 2018

data-science-for-beginners

10 Weeks, 20 Lessons, Data Science for All!

data-science-tutorials

deep-nlp-resources

Curated list of all NLP Resources

delete-github-forks

🍴 Bulk delete your GitHub forks in two simple steps!

dragon

[NeurIPS 2022] DRAGON 🐲: Deep Bidirectional Language-Knowledge Graph Pretraining

eat_tensorflow2_in_30_days

Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

fact_discovery

This repository provides code to replicate the experiments conducted in the research paper [Evaluation of Sampling Methods for Discovering Facts from Knowledge Graph Embeddings].

fkg-mini-project

Machine Learning (ML) algorithms need data in the form of feature vectors and class labels to come up with a trained model and predict new instances with that trained model. Data stored in Knowledge Graph(s) (KG) is in the form of triples. To apply ML algorithms on KG, we need to convert the individuals in the learning problem into feature vectors. One way to tackle this task is to use KG embedding.

free-programming-books

:books: Freely available programming books

free-python-books

A curated collection of free eBooks about Python

gene-association-analysis-using-kg-embeddings

generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

goingmeta

code and resources used in the Going Meta sessions

graphml-class

Full Stack Graph Machine Learning: Theory, Practice, Tools and Techniques