Machine Learning
Showing 36 of 1845 projects
Genetic Programming in Python, with a scikit-learn inspired API
A self-contained machine learning and natural language processing library written in pure Go with a dynamic computational graph.
A real-time baseline 3D multi-object tracking system using LiDAR point clouds, combining 3D Kalman filter and Hungarian algorithm.
A curated list of open source technology for agriculture, farming, and gardening.
A curated list of open source technology for agriculture, farming, and gardening.
Original DeepMind DQN 3.0 implementation for Atari game reinforcement learning, with community tweaks.
A collection of interactive machine learning experiments with Jupyter notebooks for training and browser demos for visualization.
A library for creating TensorFlow models that handle structured data with dynamic computation graphs using dynamic batching.
A curated collection of academic papers on data mining and machine learning techniques for fraud detection across various domains.
A curated collection of high-quality deep learning resources, including courses, books, papers, libraries, and datasets.
An abstraction layer over MetalPerformanceShaders for crafting and running fast neural networks on iOS using TensorFlow models.
A comprehensive Rust library for quantitative finance, offering pricing models, risk analysis, and financial data tools.
An open-source Python toolkit providing a comprehensive collection of algorithms for interpreting and explaining machine learning models and datasets.
An experimentation platform for training and researching automated agents in abstract simulated enterprise network environments using reinforcement learning.
A lambda architecture framework on Apache Spark and Kafka for building and deploying real-time large-scale machine learning applications.
Clean PyTorch implementations of imitation and reward learning algorithms for reinforcement learning.
A domain-specific language and C++ library for automatically synthesizing high-performance machine learning kernels.
A collection of models, callbacks, and datasets to extend PyTorch Lightning for applied AI/ML research and production.
An easy-to-use, state-of-the-art named-entity recognition (NER) tool based on neural networks.
An open-source computer vision tool that detects, tracks, and counts moving objects from cameras and videos.
A lightweight library providing PyTorch training tools and utilities to simplify and standardize training loops.
A BERT language model pre-trained on a large corpus of scientific papers for natural language processing tasks in scientific domains.
A curated collection of 500+ resources for data analysis and data science, covering Python, SQL, ML, visualization, roadmaps, and interview prep.
TensorLy: Tensor Learning in Python.
A Python library for simulating spiking neural networks (SNNs) using PyTorch, geared towards biologically inspired machine learning.
A neural network library for Elixir built on Nx, providing functional, model creation, and training APIs for deep learning.
A curated checklist of state-of-the-art research materials (datasets, papers, code) for interaction-aware trajectory prediction.
A unified interface and infrastructure for machine learning in R, supporting classification, regression, clustering, and survival analysis.
Python audio and music signal processing library
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
A Python library for offline deep reinforcement learning with support for state-of-the-art algorithms and user-friendly APIs.
A lightweight CoreML model for detecting NSFW content in images, specifically trained to distinguish between suggestive and explicit content.
A Python library for deep probabilistic modeling and analysis of single-cell and spatial omics data.
A high-performance machine learning library for Haskell that leverages algebraic structures for parallel, online, and fast cross-validation training.
A machine learning security engine that preemptively prevents web app and API threats using supervised and unsupervised models.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
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