Repository Index

Exploring the space of drug combinations to discover synergistic drugs using Active Learning

Java Scientific Computing Library - Signal Processing, FFT, Filters, PSD, DCT, SciPy-like APIs for JVM & Android used in Machine Learning and Data Science.

• android
• java
• python
• scipy

American Sign Language Detection is a deep learning end to end project where we can detect American Sign Language.

• asl
• keras
• tensroflow
• tflite

GoStats is a go library for math statistics mostly used in ML domains, it covers most of the statistical measures functions.

• golang
• go
• machine-learning
• stats

A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.

chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models

An Icon classifier made with TFLite Model Maker and deployed to Android with ML Model Binding.

• tensorflow-lite
• tflite
• computer-vision
• deep-learning

Conflict-free merge for DataFrames, JSON, ML models & distributed agents — powered by CRDTs. The first merge library where every operation is mathematically guaranteed to converge.

• collaborative-editing
• conflict-resolution
• crdt
• data-engineering

A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.

• art-history
• color-analysis
• color-extraction
• color-palette

A dependency-free Linear Algebra, Statistics, and Machine Learning library written from scratch in V

• ml
• statistics
• v

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

DataSet File Format (DSFF)

• dataset
• file-format
• machine-learning
• python

Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.

Estimating the number of clusters in a data set via the gap statistic. Implemented in H2O-3

• kmeans
• kmeans-clustering
• clustering
• gap-statistic

Implementation of Partitioning Around Medoids (PAM) in H2O-3

• clustering
• machine-learning
• h2o-3
• java