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머신러닝 · GitHub

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AutoMoonBot

SnowCheetos

36스타

Algorithmic trading using heterogeneous graph neural network and reinforcement learning, pre-alpha

737스타

推荐系统入门指南,全面介绍了工业级推荐系统的理论知识(王树森推荐系统公开课-基于小红书的场景讲解工业界真实的推荐系统),如何基于TensorFlow2训练模型,如何实现高性能、高并发、高可用的Golang推理微服务。Comprehensively introduced the theory of industrial recommender system, how to trainning models based on TensorFlow2, how to implement the high-performance、high-concurrency and high-available inference services base on Golang.

머신러닝Jupyter NotebookApache-2.0GitHub

treebank

someben

40스타

Ruby loader for the Stanford Sentiment Treebank (SST) parse trees and phrase labels, plus a locality-sensitive-hash index for word2vec binaries

머신러닝RubyApache-2.0GitHub

VLM-AutoYOLO

Somnusochi

278스타
VLM-AutoYOLO preview

AI Auto Annotation & YOLO Training Pipeline, End-to-end object detection auto-labeling and YOLO training platform. VLM-powered annotation with NVIDIA LocateAnything-3B, manual refinement, one-click YOLO training, video keyframe extraction, and model validation. Supports image and video.

  • 🤖 VLM auto-labeling: Open-vocabulary object detection with LocateAnything-3B
  • 🎯 SAM2 / SAM3 segmentation: Bbox → pixel-precise mask with SAM 2.1 or SAM3 text-driven detection+segmentation in one pass, BBox/Mask toggle on canvas
머신러닝PythonAGPL-3.0GitHub

tape

songlab-cal

746스타

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

머신러닝PythonBSD-3-ClauseGitHub

kospeech

sooftware

637스타

Open-Source Toolkit for End-to-End Korean Automatic Speech Recognition leveraging PyTorch and Hydra.

머신러닝PythonApache-2.0GitHub

iris-3b

speridlabs

227스타
iris-3b preview

Iris-3B: pixel-space generation & general vision learner. A 3B diffusion transformer that generates every pixel directly (no VAE); the same prior, fine-tuned, does monocular depth estimation and 4x image restoration. Paper, weights, training code and demo.

머신러닝PythonApache-2.0GitHub
347스타

source{d} datasets ("big code") for source code analysis and machine learning on source code

  • Structural commit features
  • Description: 1.6 million commits in 622 Java repositories on GitHub.
머신러닝Jupyter NotebookGitHub