Awesome GitHub RepositoriesSemi-supervised Learning Pipelines

This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i

This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular

PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative adversarial network architectures. It serves as a toolkit for training and evaluating models that utilize adversarial minimax optimization to produce synthetic data, offering a structured environment for exploring complex generative tasks within the PyTorch ecosystem. The library distinguishes itself through a comprehensive suite of image synthesis and manipulation capabilities, including super-resolution, inpainting, and cross-domain style translation. It supports advanced training m

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

Pyannote.audio is a PyTorch toolkit for speaker diarization, speaker identification, and speech activity detection. Its primary purpose is to partition audio recordings into segments and assign each segment to a specific speaker identity to determine who spoke when. The project includes a framework for classifying speaker identities and a pipeline for distinguishing human speech from background noise. It provides specialized tools for handling symmetric-overlap speech, where multiple speakers talk simultaneously, and employs learnable band-pass filters for raw waveform feature extraction. Th

This is a graph convolutional network library designed for performing node and graph classification on graph-structured data. It functions as a framework for generating graph embeddings and implementing spectral convolutional neural networks to predict labels for nodes and entire graph structures. The library provides specialized tools for spectral graph convolutions, utilizing Chebyshev polynomial approximations to perform feature aggregation. It includes a multi-graph processing framework that manages batches of different graph instances through block-diagonal adjacency matrices and pooling

pygcn ist eine PyTorch-Bibliothek und ein Framework zur Implementierung von Graph Convolutional Networks. Sie bietet Tools für semi-überwachte Knotenklassifizierung und die Generierung von Knoteneinbettungen aus graphstrukturierten Daten. Das System konvertiert Graph-Knoten in niedrigdimensionale Vektoren basierend auf Nachbarschaftsmustern und lokalen Ähnlichkeiten. Es ermöglicht die Vorhersage von Knoten-Labels durch die Nutzung sowohl einer kleinen Menge gelabelter Beispiele als auch der gesamten Graphtopologie. Die Bibliothek deckt relationale Datenanalyse und semi-überwachtes Graph-Learning ab. Sie enthält Rechenprimitive für Message-Passing, Adjazenz-Transformation und symmetrische Normalisierung.

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

Dieses Projekt ist ein selbstüberwachtes kontrastives Lern-Framework, das darauf ausgelegt ist, Deep-Learning-Modelle darauf zu trainieren, visuelle Repräsentationen aus Bildern zu lernen, ohne menschlich bereitgestellte Labels zu verwenden. Es bietet ein System zur Entwicklung vortrainierter visueller Repräsentationsmodelle, die für nachgelagerte Computer-Vision-Aufgaben angepasst werden können. Das Framework enthält Tools für semi-überwachte Bildklassifizierung, die große ungelabelte Datensätze mit kleinen gelabelten Sets kombiniert, um die Genauigkeit zu verbessern. Es bietet zudem ein Linear-Probe-Evaluierungstool, um die Qualität gelernter Bildmerkmale zu bewerten, indem ein einfacher linearer Klassifikator auf Basis eingefrorener Repräsentationen trainiert wird. Die Codebasis deckt verteiltes Deep-Learning-Training und Hardwarebeschleunigung ab, um große Batch-Größen zu handhaben, neben Optimierungsprimitiven wie Cosine-Decay-Learning-Rate-Scheduling und Weight-Decay-Regularisierung. Sie bietet zudem Dienstprogramme für das Modellmanagement, einschließlich der Konvertierung vortrainierter Checkpoints zwischen verschiedenen Deep-Learning-Framework-Formaten und Tools für das Model-Deployment. Die Implementierung wird als Sammlung von Jupyter Notebooks bereitgestellt.

Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the constr

VideoPose3D is a machine learning framework designed for 3D human pose estimation. It functions as a motion reconstruction tool that predicts 3D joint positions from 2D video sequences using a temporal convolutional network to process body movement over time. The project includes a semi-supervised learning pipeline that improves pose accuracy by combining labeled datasets with unlabeled video data and projection consistency loss. It also features a video pose visualizer capable of rendering 3D skeleton reconstructions and 2D keypoints as overlays on original footage. The framework covers the

GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for transforming complex network nodes into low-dimensional vector spaces, enabling the integration of relational graph data into machine learning workflows. The library functions as a dimensionality reduction toolkit and network topology analysis tool. It uses matrix-factorization techniques to preserve global connectivity and employs random-walk sampling with skip-gram based vector optimization to learn numerical representations of nodes. The framework covers several domain-specific

GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data. The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and

Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations of popular graph representation learning algorithms and research papers. The project covers core graph machine learning tasks including semi-supervised node classification, inductive and unsupervised node embedding generation, and neighborhood feature aggregation. The library supports diverse algorithmic approaches for processing network structures, ranging from shared-parameter graph convolutions and attention-weighted neighborhood aggregation to spectral Chebyshev filtering

Dieses Repository dient als Bildungsressource für die Implementierung von Graph Neural Networks mit Python. Es bietet eine Sammlung strukturierter Codebeispiele und Tutorials, die Entwickler durch den Prozess des Aufbaus und Trainings von Machine-Learning-Modellen führen, die auf komplexen, miteinander verbundenen Datensätzen operieren. Das Projekt deckt die Kernmechaniken des graphbasierten Deep Learnings ab, einschließlich Message-Passing-Architekturen, Feature-Aggregation und dem Stapeln von Convolutional Layers. Es demonstriert, wie man nicht-euklidische Daten als statische Graphen darstellt und wie man den Speicher während des Trainings durch Mini-Batch-Sampling-Techniken verwaltet. Die enthaltenen Implementierungen adressieren gängige analytische Aufgaben wie Knotenklassifizierung, Graph-Strukturvorhersage und die Integration heterogener Datenquellen in einheitliche Modelle. Das Repository ist als eine Reihe praktischer Übungen organisiert, die theoretische Graph-Konzepte in funktionale Machine-Learning-Workflows übersetzen.