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Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.
Python3 script that transfers files in Google Colab 10-50x faster, using multi-threaded parallel copying between Google Drive locations.
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.
American Sign Language Detection is a deep learning end to end project where we can detect American Sign Language.
Universal 1d/2d data containers with Transformers functionality for data analysis.
GoStats is a go library for math statistics mostly used in ML domains, it covers most of the statistical measures functions.
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.
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.
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.
A dependency-free Linear Algebra, Statistics, and Machine Learning library written from scratch in V
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
DataSet File Format (DSFF)
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
Implementation of Partitioning Around Medoids (PAM) in H2O-3