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机器学习 · GitHub

发现模型训练、评估与推理框架,比较深度学习和机器学习项目的社区关注度。

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BlueRingsLabs

175星标
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Free Course : Data, Machine Learning, Deep Learning and AI Engineering, the long way round: the math, the code, the systems and the security. 173 lessons, 166 runnable labs, no shortcuts. Free under CC BY-NC 4.0.

ChatSpatial

cafferychen777

46星标
ChatSpatial preview

MCP server for spatial transcriptomics analysis through natural language interfaces.

  • Data Loading & Preprocessing - Scanpy I/O, QC, Normalization, HVG, PCA, Neighbors
  • Visualization - Spatial plots, Embedding plots, Gene expression overlays

cordon

calebevans

165星标

Reduce logs to their semantic anomalies

  • Semantic Analysis: Uses transformer models to understand log content meaning, not just keyword matching
  • k-NN Density Scoring: Identifies anomalies using k-NN distance in embedding space
机器学习PythonApache-2.0GitHub

tweetopic

centre-for-humanities-computing

37星标
tweetopic preview

Blazing fast topic modelling for short texts.

  • Scalable :collision:
  • High consistency and coherence :dart:
211星标

It is a project of detecting phishing websites which are main cause of cyber security attacks. It is done using Machine learning with Python

机器学习Jupyter NotebookMITGitHub

Swarm

christopherkarani

588星标

Type-safe tools, on-device inference, multi-agent workflows, memory, and guardrails, in one Swift-native runtime

  • Conversation and memory - Preserve multi-turn state with Conversation; choose conversation, sliding-window, vector, summary, or hybrid memory. Sessions snapshot to portable JSON or FileSession files.
  • Resilience - Retry with backoff and Retry-After, circuit breakers, rate limits, FailoverProvider chains, and tool-loop detection.

RiskInDroid

ClaudiuGeorgiu

163星标
RiskInDroid preview

A tool for quantitative risk analysis of Android applications based on machine learning techniques

awesome-llms-fine-tuning

Curated-Awesome-Lists

532星标

Explore a comprehensive collection of resources, tutorials, papers, tools, and best practices for fine-tuning Large Language Models (LLMs). Perfect for ML practitioners and researchers!

d2l-vi

d2l-ai

666星标

Một cuốn sách về Học Sâu đề cập đến nhiều framework phổ biến, được sử dụng trên 300 trường Đại học từ 55 đất nước bao gồm MIT, Stanford, Harvard, và Cambridge.

llm-algo-leetcode

datawhalechina

746星标
llm-algo-leetcode preview

LLM algorithm practice lab with theory, solutions, and test cases.《大模型算法与系统教程》面向大模型入门到进阶的算法实战教程,覆盖原理讲解、答案解析、测试用例与 CUDA/Triton 实战。

  • Notebook-first: Each lesson is organized around runnable code, exercises, answer cells, and basic validation.
  • Goal-oriented entry: Start from Part 02 for implementation practice, or follow the architecture, post-training, inference, performance, and operator routes.
机器学习Jupyter NotebookGitHub
185星标
SlicerAutomatedDentalTools preview

A 3D Slicer extension to use AMASSS, ALI-CBCT and ALI-IOS

  • Simple: Perform time consuming task with a few clicks.
  • Automatic: Automate important Dental and Cranio Facial analysis tasks.

Baobab

deep-learning-indaba

61星标
Baobab preview

Baobab is an open source multi-tenant web application designed to facilitate the application and selection process for large scale meetings within the machine learning and artificial intelligence communities globally. Supported by the Deep Learning Indaba

机器学习PythonApache-2.0GitHub

AnomaVision

DeepKnowledge1

39星标
AnomaVision preview

Production-ready visual anomaly detection from normal images, featuring PaDiM, ultra-light PatchCore, automated edge deployment, and optimized ONNX, OpenVINO, TensorRT INT8, Hailo, and KV260 support.

机器学习Jupyter NotebookMITGitHub

deeplay

DeepTrackAI

28星标

Deeplay is a deep learning library in Python that extends PyTorch with additional functionalities focused on modularity and reusability.

机器学习Jupyter NotebookMITGitHub

fhiry

dermatologist

51星标

FHIR to pandas dataframe for data analytics, AI and ML!

  • Flatten FHIR Bundles/NDJSON to DataFrames for analytics and ML
  • Import from FHIR Server via FHIR Search API
机器学习Jupyter NotebookMITGitHub
90星标
Unsupervised-Defect-Detection-Project-Based-on-VAE-GAN-Architecture preview

本项目实现了一种基于 VAE-CycleGAN 的图像重建无监督缺陷检测算法。该算法结合了变分自编码器 (VAE) 和 CycleGAN 的优势,无需标注数据即可检测图像中的缺陷/异常。This project implements an unsupervised defect detection algorithm for image reconstruction based on VAE-CycleGAN. This algorithm combines the advantages of variational autoencoders (VAE) and CycleGAN to detect defects in images without any supervision.

  • Unsupervised learning: No need for labeled anomaly data
  • VAE-based image reconstruction
机器学习PythonGPL-3.0GitHub