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The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

VIP cheatsheets for Stanford's CS 229 Machine Learning

An opinionated, actively maintained collection of resources for software developers learning to build and ship generative AI and agentic systems.

A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.

txt inference code, following DINOv2 Meets Text: A Unified Framework for Image- and Pixel-Level Vision-Language Alignment.

This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation, for both TF v1 & v2.

Related Codes: - Large language model evaluation: [llm-eval] - Large language model enhancement: [llm-enhance] - Robust machine learning: [robustlearn: robust machine learning] - Semi-supervised learning: [USB: unified semi-supervised learning benchmark] | [TorchSSL: a unified…

100-Days-Of-ML-Code中文版

> > > Abstract: We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. , freckles, hair), and it enables intuitive, scale-specific control of the synthesis.

This repository provides a research-grade, unsupervised anomaly detection framework for microservice logs, combining multi-modal embeddings, graph attention networks, hybrid ensembles with GMM thresholding, streaming simulation, and explainability (SHAP + gradients).