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).
RuVector provides High Performance, Real-Time decisions and agent memory , Self-Learning Ai, Vector GNN DB built in Rust.
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,
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This repository contains implementations and illustrative code to accompany DeepMind publications.
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Each implementation: - ✅ Uses only NumPy (no deep learning frameworks) for educational clarity - ✅ Includes synthetic/bootstrapped data for immediate execution - ✅ Provides extensive visualizations and explanations - ✅ Demonstrates core concepts from each paper - ✅ Runs in…
Python package built to ease deep learning on graph, on top of existing DL frameworks.
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PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
Must-read papers on graph neural networks (GNN)