Machine Learning
Showing 36 of 1845 projects
An experimental library that converts TensorFlow functions and graphs into JAX functions for reuse and fine-tuning within JAX codebases.
An open-source benchmark solution for the Kaggle TGS Salt Identification Challenge using semantic segmentation.
A declarative data-flow programming framework built on Zenoh for building applications that span from cloud to edge devices.
Flax implementations and pretrained checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX.
A Ruby interface to XGBoost, providing high-performance gradient boosting for machine learning tasks.
A curated list of algorithms and academic papers for auditing black-box algorithms like recommendation systems and classifiers.
A Julia library providing a consistent API for common machine learning algorithms, designed for practitioners working with in-memory datasets.
Library for machine learning stacking generalization.
A freely usable dataset of over 5,000 labeled clothing images across 20 categories for machine learning projects.
A PyTorch-based deep learning library for building and training spiking convolutional neural networks with hardware deployment support.
A machine learning and optimization framework for Objective-C and Swift, focused on regression and multi-objective evolutionary algorithms.
A Rust tool that machine-learns efficient password mangling rules for John the Ripper or Hashcat from a dictionary and password list.
A collection of scripts for training random forests and sparse filtering models on Kaggle datasets.
A deep belief net and deep learning implementation written in F# with GPU acceleration via Alea.cuBase.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
A dataset of NBA game summaries aligned with box- and line-scores for data-to-text generation research.
A parallel Random Forest implementation in Go for classification and regression tasks.
A simple and functional machine learning library for Erlang, Elixir, and Gleam projects.
A PHP library for building predictions using linear regression with simple data fitting.
A Haskell library for building and training feed-forward neural networks with automatic differentiation.
A lightweight Clojure wrapper for TensorFlow's Java API, providing idiomatic access to machine learning operations.
A cross-platform CLI tool for cleaning and improving text datasets for machine learning, with fast operations and LLM-based filtering.
A GPU-accelerated (CUDA) C++ template library for building and training artificial neural networks, including self-organizing maps and back-propagation networks.
A DSL-based library for unified tensor reshaping, squeezing, expanding, and transposing in JAX, TensorFlow, and NumPy.
A Swift kernel for Google Colaboratory that enables Swift programming in the browser with GPU support for machine learning.
A Python framework for building and deploying serverless data and ML pipelines on AWS using AWS CDK.
A Swift library for accelerated tensor operations and dynamic neural networks with automatic differentiation, supporting all Apple platforms and Linux.
A tool for automatically detecting and suggesting mitigation for object, attribute, and geography-based biases in visual datasets.
A lightweight Bayesian optimization library built on JAX for efficient optimization of expensive-to-evaluate functions.
A Python feature engineering engine that internally manages past dependent values for continuous calculation of time-based features.
A Scalding library for machine learning and statistical analysis, featuring Mahout vector integration, K-Means clustering, and Naive-Bayes classifiers.
A Julia package for efficient large-scale Gaussian Mixture Models with support for diagonal/full covariance, parallel training, and variational Bayes.
High-performance matrix and numerical computing library for Delphi and Free Pascal with linear algebra, ML primitives, and optimized kernels.
A performant JAX reimplementation of the UniRep model for generating protein sequence representations.
A serverless machine learning framework that scales algorithms across cloud lambda functions.
A curated list of resources for molecular docking, protein-protein docking, and related computational biology tasks.
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