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머신러닝 · GitHub

모델 학습, 평가, 추론 프레임워크를 찾고 커뮤니티의 관심도를 비교하세요.

유용한 GitHub 프로젝트

4,624 프로젝트

DigitSuo

tetsuo-ai

48스타
DigitSuo preview

handwritten digit recognition system using a custom neural network architecture. Built with C, it features both training capabilities and an interactive recognition interface. >98% accuracy on the MNIST dataset.

tevatron

texttron

753스타

Tevatron - Unified Document Retrieval Toolkit across Scale, Language, and Modality. Demo in SIGIR 2023, SIGIR 2025.

  • Training billion-scale LLM neural retriever on GPUs and TPUs.
  • Parameter efficient tuning with LoRA.
머신러닝PythonApache-2.0GitHub

sslsv

theolepage

41스타
sslsv preview

Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker Verification (SV).

  • Supervised and Self-supervised datasets (siamese and DINO sampling)
  • Audio augmentation (noise and reverberation)

lacuna-research-mcp

tiptreesystems

63스타
lacuna-research-mcp preview

MCP server for Lacuna, a research map for machine learning: search directions, papers, authors, venues, and AI-generated research proposals.

571스타
fire-detection-cnn preview

real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)

pix2code

tonybeltramelli

12,009스타

pix2code: Generating Code from a Graphical User Interface Screenshot

머신러닝PythonApache-2.0GitHub

tsl

TorchSpatiotemporal

392스타

tsl: a PyTorch library for processing spatiotemporal data.

  • Create Custom Models and Datasets Easily build your own custom models and datasets for spatiotemporal data analysis. Whether you're working with sensor networks, environmental data, or any other spatiotemporal domain, tsl's high-level APIs empower you to develop tailored solutions.
  • Access a Wealth of Existing Datasets and Models Leverage a vast collection of datasets and models from the spatiotemporal data processing literature. Explore and benchmark against state-of-the-art baselines, and test your brand new model on widely used public datasets.

tmrl

trackmania-rl

750스타
tmrl preview

Reinforcement Learning for real-time applications

  • Analog control from screenshots: The tmrl example pipeline trains policies that are able to drive from raw screenshots captured in real-time. For beginners, we also provide simpler rangefinder ("LIDAR") observations, which are less potent but easier to learn from. The example pipeline controls the game via a virtual gamepad, which enables analog actions.
  • Models: To process LIDAR measurements, the example tmrl pipeline uses a Multi-Layer Perceptron (MLP). To process raw camera images (snapshots), it uses a Convolutional Neural Network (CNN). These models learn the physics of the game from histories or observations equally spaced in time.

dvc

treeverse

15,971스타

🦉 데이터 버전 관리와 머신러닝 실험

머신러닝PythonApache-2.0GitHub

tadpole

tum-pbs

29스타
tadpole preview

[NeurIPS2026] Flexible scientific foundation models trained with synthetic online data generation

머신러닝PythonApache-2.0GitHub

torch-frft

tunakasif

61스타

PyTorch implementation of the fractional Fourier transform with trainable transform order.

skills

ultralytics

38스타

Agent skills for Ultralytics YOLO, covering models, datasets, training, tuning, inference, tracking, and export.

77스타
investment-funnel preview

Investment Funnel 📈 is an open-source python platform designed for an easy development and backtesting of outperforming investment strategies.

Torchelie

Vermeille

111스타

Torchélie is a set of utility functions, layers, losses, models, trainers and other things for PyTorch.

geo-clip

VicenteVivan

419스타
geo-clip preview

This is an official PyTorch implementation of our NeurIPS 2023 paper "GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization"

dreamer_v4

vijayabhaskar-ev

40스타
dreamer_v4 preview

Code, results and checkpoints for the paper "Silent Pipeline Defects in World-Model RL: A Dreamer 4 Case Study": a PyTorch reimplementation of Dreamer 4, two silent defects that passed 12 checks, and three cheap checks that catch them

283스타
modern-llm-notebook preview

A hands-on course on modern LLM architectures, training, and inference, with step-by-step PyTorch implementations and runnable notebooks.

머신러닝Jupyter NotebookGitHub