Awesome GitHub RepositoriesSentiment & Topic Analysis

Azure Docs is the official technical documentation repository for Microsoft Azure, the cloud computing platform. It provides comprehensive guidance on the full spectrum of Azure services, covering everything from core infrastructure components like virtual machines, Kubernetes clusters, and serverless computing to platform services for AI, machine learning, data analytics, and storage. The documentation details how to provision, manage, and govern cloud resources at scale, including policy enforcement, identity management, and cost optimization. The documentation distinguishes Azure through i

nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing techniques to real-world tasks. It provides standardized workflows and code examples for developing NLP pipelines, from dataset preparation and model training to performance evaluation. The project focuses on the practical application of transformer-based models, offering patterns for fine-tuning pretrained architectures for tasks such as text classification, named entity recognition, and question answering. It also includes a toolkit for model interpretability, allowing users

Mmlspark es un framework distribuido para ejecutar modelos de machine learning, transformaciones de datos e integraciones de servicios de IA a través de clústeres de Apache Spark. Funciona como una biblioteca de machine learning distribuida y orquestador de pipelines, permitiendo a los usuarios integrar servicios cognitivos preentrenados y modelos personalizados en flujos de trabajo de procesamiento por lotes y streaming a gran escala. El proyecto se distingue por su capacidad para incorporar servicios de IA externos y API web directamente en pipelines de big data para análisis de texto y visión. Proporciona un framework de entrenamiento de modelos escalable que coordina tareas de potenciación de gradiente (gradient boosting) y clasificación a través de clústeres de cómputo elásticamente redimensionables, utilizando aceleración por hardware para la inferencia de modelos distribuida. El conjunto de herramientas cubre una amplia gama de capacidades, incluyendo análisis de contenido multimodal para imagen, voz y texto, así como detección de anomalías avanzada para datos de series temporales y multivariados. Incluye utilidades para la featurización de datos, la ejecución de modelos ONNX y herramientas de IA responsable para la auditoría de equidad de modelos y la interpretación de predicciones utilizando valores de contribución aditivos. El framework también proporciona una interfaz de acceso a datos unificada para leer y escribir a través de diversas bases de datos y sistemas de almacenamiento en la nube.

This project is a collection of structured study notes and conceptual breakdowns designed for the AWS Certified Cloud Practitioner exam. It serves as a technical reference and study guide, organizing cloud service details and architectural principles to assist in certification preparation. The knowledge base is built using markdown files and includes curated cheat sheets and interactive mind-map visualizations. These tools map complex certification topics into visual hierarchies to enable drill-down study paths and rapid revision. The materials cover a wide range of cloud capabilities, inclu

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing