Awesome GitHub RepositoriesSpatial Downsampling

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 library provides a comprehensive collection of modular building blocks and research-backed architectures for implementing vision transformers within the PyTorch framework. It serves as a centralized repository for constructing, training, and analyzing attention-based models, offering a wide array of specialized variants designed for image classification and visual representation learning. The project distinguishes itself through a focus on architectural efficiency and flexibility, supporting diverse input formats including non-square images and volumetric data like video. It incorporates

Thanos is a CNCF cloud native monitoring tool that provides a highly available and scalable extension to the Prometheus ecosystem. It functions as a global query engine, a long-term storage system, and a metric downsampler. The project enables a unified interface to aggregate and query metrics across multiple distributed clusters from a single view. It maintains historical data beyond local retention limits by persisting time-series metrics in object storage and eliminates data gaps by merging metrics from redundant server pairs. The system includes capabilities for reducing the resolution o

This project is a comprehensive library of practical Python code examples and patterns. It provides a collection of scripts and snippets designed to demonstrate a wide range of programming tasks, from basic syntax to advanced implementation patterns. The repository focuses on several core domains, including the implementation of concurrency and multithreading examples, data analysis snippets for cleaning and manipulating tabular data, and various data visualization examples. It also covers automation scripts for file system management and a variety of general programming patterns. Additional

mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and restoration. It provides the infrastructure to build and train models for tasks such as text-to-image and text-to-video generation, 3D-aware content synthesis, and high-fidelity image translation using diffusion models and generative adversarial networks. The project distinguishes itself through specialized capabilities for generative model personalization, including techniques for fine-tuning subjects and styles. It also supports advanced visual manipulations such as latent space interp

这是一个全面的教育课程，旨在教授使用 Python 编程语言的机器学习基础知识。它提供了一个结构化的课程，涵盖监督学习、无监督学习和深度学习的实现与理论。 该课程通过结合可执行代码和技术教程的交互式 notebook 提供。它包括用于构建神经网络架构、实现分类和回归模型，以及利用聚类技术在未标记数据中发现模式的专门指南。 这些材料涵盖了完整的机器学习工作流程，包括数据预处理和分类编码、模型训练和超参数调优，以及性能评估。它还具有用于可视化模型行为的工具，例如决策边界绘图和决策树图。

Code release for ConvNeXt model

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

OpenTSDB 是一个分布式时间序列数据库和指标引擎，专为存储和管理海量高基数系统指标而设计。它作为一个数据存储和分析平台，支持跨分布式集群的大规模指标摄取和基础设施性能监控。 该系统以其支持 HBase、Cassandra 和 Google Bigtable 等多个后端的分布式存储抽象而著称。它利用分层指标树来组织时间序列，并采用数字标识符索引来减少存储占用并加速标记指标的查找。 该项目涵盖了广泛的能力领域，包括具有分布式百分位数计算和降采样功能的时间序列数据分析，以及全面的元数据管理。它提供用于数据摄取和查询的 API 集成、用于性能优化的堆外缓存，以及用于数据完整性审计和异常分析的工具。 该系统通过用于数据库管理和指标树同步的命令行界面进行管理。