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apache-spark-2x-for-java-developers

Apache Spark 2x for Java Developers, published by Packt

arabesque

Scalable Graph Mining

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

bitcoin_volatility_forecasting

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

cgga

Cancer subtype identification by consensus guided graph autoencoder

cheatsheets

My cheatsheets

chinese-whispers

Implementation of the Chinese Whispers graph clustering algorithm

cisticola

cisticola is an open source interactive platform for performing quick scientific graph operations

class

clinical-notes-diagnosis-dl-nlp

cnn-from-scratch

A Convolutional Neural Network implemented from scratch (using only numpy) in Python.

coursera-stanford-ml-python

Coursera/Stanford Machine Learning course assignments in python

cppbook-sourcecode

Source code from the book Fundamentals of C++ Programming by Richard Halterman

cryptocurrencyprediction

Predict Cryptocurrency Price with Deep Learning

cs231n.github.io

Public facing notes page

cypher-dsl

A Java DSL for the Cypher Query Language and an optional Query DSL mode

data-science--cheat-sheet

Cheat Sheets

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

decagon

Graph convolutional neural network for multirelational link prediction

deep-learning-for-medical-applications

Deep Learning Papers on Medical Image Analysis

digital-horticulture

disease_gene_prioritization_gcn

PGCN: Disease gene prioritization by disease and gene embedding through GCN

dltk

Deep Learning Toolkit for Medical Image Analysis

gbench

Graph Dataset Benchmarking

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gem

gephi-toolkit-demos

Quick demos using the Toolkit

giraph

Mirror of Apache Giraph

graft

grami

GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.