Machine Learning
Showing 36 of 601 projects
A curated collection of Python libraries, tutorials, and tools for data science, from data wrangling to machine learning and visualization.
A modular container build system providing the latest AI/ML packages for NVIDIA Jetson and JetPack-L4T.
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.
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.
A unified, comprehensive, and efficient Python/PyTorch library for reproducing and developing recommendation algorithms.
An open-source library for training and deploying deep learning recommendation models with sparse data at scale using multi-GPU support.
A large-scale dataset of images with object segmentation, bounding boxes, and visual relationship annotations.
A command-line utility for querying and monitoring NVIDIA GPU status with a human-readable output.
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.
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 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 modular quantitative finance framework for data collection, analysis, strategy backtesting, and machine learning across multiple markets.
A fast parallel implementation of the Connectionist Temporal Classification (CTC) loss function for CPU and GPU.
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Original implementation and hyperparameters for the 2014 paper "Generative Adversarial Networks" (GANs).
A Python library offering scalable and user-friendly implementations of state-of-the-art neural forecasting models.
A fast, single-file similarity search and clustering engine for vectors and arbitrary objects with multi-language bindings.
An open-source LLMOps platform for prompt management, evaluation, and observability to build reliable LLM applications faster.
A collection of transformer protein language models for predicting structure, function, and designing proteins from sequences.
A lightweight, single-binary Rust inference server providing 100% OpenAI-API compatible endpoints for local GGUF models.
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 Unity-based simulator for training self-driving car models using deep learning.
A research framework for reinforcement learning providing modular building blocks and reference agent implementations.
A local-first, ML-powered desktop application for translating manga, built in Rust with automated text detection, OCR, inpainting, and LLM translation.
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 browser-based tool that lets anyone create machine learning models without writing code, using TensorFlow.js.
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