Machine Learning
Showing 36 of 1479 projects
A fast and simple OCR library written in Swift, optimized for recognizing short alphanumeric codes like gift cards.
Fast and simple OCR library for iOS/macOS using neural networks, optimized for short alphanumeric codes.
A curated collection of Python libraries, tutorials, and tools for data science, from data wrangling to machine learning and visualization.
A local-first, ML-powered desktop application for translating manga, built in Rust with automated text detection, OCR, inpainting, and LLM translation.
A comprehensive .NET framework for machine learning, computer vision, statistics, and scientific computing.
A Python library using machine learning for accurate and scalable fuzzy matching, record deduplication, and entity resolution on structured data.
A unified, comprehensive, and efficient Python/PyTorch library for reproducing and developing recommendation algorithms.
Official code repository for the 'Machine Learning with TensorFlow' book with practical examples.
A library for probabilistic reasoning and statistical analysis integrated with TensorFlow and JAX.
A suite of visual diagnostic tools that extend scikit-learn to steer machine learning model selection through visualizations.
An open-source library for training and deploying deep learning recommendation models with sparse data at scale using multi-GPU support.
A command-line utility for querying and monitoring NVIDIA GPU status with a human-readable output.
A large-scale dataset of images with object segmentation, bounding boxes, and visual relationship annotations.
A free course teaching diffusion models theory and hands-on implementation using Hugging Face's Diffusers library.
An AI experimentation platform built on Minecraft for training and researching intelligent agents in complex 3D environments.
A comprehensive collection of tutorials, examples, and resources for understanding and solving machine learning and pattern classification problems.
An open-source LLMOps platform for prompt management, evaluation, and observability to build reliable LLM applications faster.
A web application for training deep learning models with a focus on computer vision tasks.
An open-source CLI tool for implementing CI/CD workflows with a focus on MLOps, automating ML experiments and reporting.
A modular quantitative finance framework for data collection, analysis, strategy backtesting, and machine learning across multiple markets.
A fast, single-file similarity search and clustering engine for vectors and arbitrary objects with multi-language bindings.
A Python library offering scalable and user-friendly implementations of state-of-the-art neural forecasting models.
A state-of-the-art Natural Language Processing library built on Apache Spark, offering 100,000+ pretrained models and pipelines in 200+ languages.
Course materials for the Johns Hopkins Data Science Specialization on Coursera.
Course materials for the Johns Hopkins Data Science Specialization on Coursera.
A collection of transformer protein language models for predicting structure, function, and designing proteins from sequences.
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Original implementation and hyperparameters for the 2014 paper "Generative Adversarial Networks" (GANs).
A fast parallel implementation of the Connectionist Temporal Classification (CTC) loss function for CPU and GPU.
A curated list of practical resources for responsible machine learning, covering interpretability, governance, safety, and ethics.
An open-source solution for continuous validation of machine learning models and data, from research to production.
An open-source cross-platform performance library of basic building blocks for deep learning applications, optimized for CPUs and GPUs.
A research framework for reinforcement learning providing modular building blocks and reference agent implementations.
A Unity-based simulator for training self-driving car models using deep learning.
An open-source threat hunting platform with advanced analytics capabilities built on ELK stack, Apache Spark, and Jupyter notebooks.
A curated list of satellite and aerial imagery datasets with annotations for computer vision and deep learning tasks.
A highly efficient, scalable Gaussian process library implemented in PyTorch with GPU acceleration and modular design.
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