Train, evaluate, and run machine learning models.
Ali-hey-0
A mental-model-first field guide to Audio AI — from raw waveforms to production voice systems. Covers ASR, TTS, audio classification, Transformer architectures (CTC/Seq2Seq), fine-tuning, and real-world pipelines like voice assistants and meeting transcription. Bilingual (EN/FA).
alirezatheh
A keyphrase extractor for Persian
AmusementClub
Efficient CPU/GPU ML Runtimes for VapourSynth (with built-in support for waifu2x, DPIR, RealESRGANv2/v3, Real-CUGAN, RIFE, SCUNet, ArtCNN and more!)
AndreiBarsan
Master's Thesis on Simultaneous Localization and Mapping in dynamic environments. Separately reconstructs both the static environment and the dynamic objects from it, such as cars.
AndresCasado
Code for PERGAMO, a two step approach to learn deformable models
andrewekhalel
Machine Learning and Computer Vision Engineer - Technical Interview Questions
Panoptic Segmentation Resources List
augmentedstartups
Easy & Modular Computer Vision Detectors, Trackers & SAM - Run YOLOv9,v8,v7,v6,v5,R,X in under 10 lines of code.
axondeepseg
Axon/Myelin segmentation using Deep Learning
Bartzi
Code for the AAAI 2018 publication "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition"
bat
A Bayesian Analysis Toolkit in Julia
BlueRingsLabs
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.
cafferychen777
MCP server for spatial transcriptomics analysis through natural language interfaces.
calebevans
Reduce logs to their semantic anomalies
centre-for-humanities-computing
Blazing fast topic modelling for short texts.
chamanthmvs
It is a project of detecting phishing websites which are main cause of cyber security attacks. It is done using Machine learning with Python
christopherkarani
Type-safe tools, on-device inference, multi-agent workflows, memory, and guardrails, in one Swift-native runtime
ClaudiuGeorgiu
A tool for quantitative risk analysis of Android applications based on machine learning techniques
Confusezius
PyTorch Implementation for Deep Metric Learning Pipelines
Curated-Awesome-Lists
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!
darkanita
MLOps Workshop using Weights and Bias (Wandb) and Github Actions.
datawhalechina
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。
DCBIA-OrthoLab
A 3D Slicer extension to use AMASSS, ALI-CBCT and ALI-IOS
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.
