zsigacsaba Goto Github PK

3d-force-graph

3D force-directed graph component using ThreeJS/WebGL

anaconda-notebook

Docker image for an IPython 3/Jupyter Notebook and Terminal with full Anaconda Install

app-python

https://graphacademy.neo4j.com/courses/app-python

awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.

awesome-multimodal-knowledge-graph

A curated list of AWESOME papers, datasets and tutorials within Multimodal Knowledge Graph.

cuda-samples

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

d3-force-cluster

cluster force module for d3.v4 `forceSimulation`

d3-jupyter-tutorial

d3fdgraph

d3 interactive animated force-directed graphs in a jupyter notebook

dash-sample-apps

Open-source demos hosted on Dash Gallery

designingwithml

A repository of example implementations for interesting ml concepts

docker-neo4j

Docker Images for the Neo4j Graph Database

explainerdashboard

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

fiftyone-examples

Examples of using FiftyOne

graph_create_structures

A generic library for creating graph data structures and performing operations on them. It supports different kinds of graphs such as directed graphs, acyclic graphs, or trees.

graphapp-starter-react

A Graph App Starter Kit for React - Clone, connect and code...

graphkit-learn

A python package for graph kernels, graph edit distances, and graph pre-image problem.

guidedlda

semi supervised guided topic model with custom guidedLDA

interpret-text

A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.

intuitivetextmining

code and resources for the book Intuitive Text Mining

knowledge-graph-embeddings-to-implement-explainability

Knowledge Graph Embeddings (KGE) to implement Explainable Artificial Intelligence. As AI develops users must know how algorithms make their decisions, especially for hazardous tasks such as driverless cars. Knowledge graphs are an inherently understandable form of text-based data created as an interconnected network of information. These can be converted into KGE by transforming the unqiue entites in the graph to vector representations. With these, predictions were made for missing/incorrect links in the network and further explainations were made by plotting the clusters of the data. Knowledge graphs and their embedded models were researched and four of these KGE were created and tested by their ability to rank the correct links from a Covid-19 dataset. This dataset was extracted from research papers about the virus to retrieve information quicker. The model which was most accurate was used to implement knowledge graph completion and explainability of the dataset using visual and textual interpretations. A 29,000-word thesis was written to describe the work done through the researching, testing and interpreting of this project.

knowledge_graph

test neo4j as visualization tool for graph data

mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

movies-python-bolt

Neo4j Movies Example application with Flask backend using the neo4j-python-driver

nlp-with-python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

pandas-profiling

Create HTML profiling reports from pandas DataFrame objects

python-docs-samples

Code samples used on cloud.google.com

pytorch-lightning

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

reddit-selfposts-blog

scattertext-pydata

Notebooks for the Seattle PyData 2017 talk on Scattertext