搜索结果：人工智能

Dive into LLMs is a comprehensive, practice-oriented tutorial series for large language models, developed from Shanghai Jiao Tong University's AI Security Technology course with Huawei Ascend support. It covers the full technical stack from fine-tuning to security alignment, offering complete learning resources and domestic hardware adaptation. 原文翻译： Dive into LLMs 是由上海交通大学《人工智能安全技术》课程拓展而来的大模型编程实践教程系列，获得华为昇腾社区支持。它以实践为导向，覆盖从微调到安全对齐的全技术栈，提供完整学习资源并适配国产硬件。

This repository provides comprehensive tools, examples, and resources for developing Hebrew-language Generative AI applications, with a focus on Large Language Models (LLMs). It includes installation instructions, practical examples, and community support resources. 原文翻译： 本资源库提供全面的工具、示例和资源，用于开发希伯来语生成式人工智能应用，重点关注大型语言模型（LLMs）。包含安装说明、实践示例和社区支持资源。

Auditi is an open-source platform for evaluating and improving LLM applications through automated LLM-as-a-judge evaluation, human annotation workflows, and advanced analytics. 原文翻译： Auditi是一个开源平台，通过自

GEO (Generative Engine Optimization) is a systematic strategy to make brand information understood, prioritized, and integrated into AI-generated answers, shifting focus from traditional search rankin

RAG-Anything is an all-in-one RAG framework developed by HKUDS at the University of Hong Kong, open-sourced on GitHub. It aims to enhance LLM performance by integrating retrieval and generation, addre

This comprehensive guide explains Retrieval-Augmented Generation (RAG) as a solution to large language models' knowledge cutoff and hallucination issues, detailing its five-generation evolution, pract

RAG投毒是一种针对检索增强生成系统的攻击方式，攻击者通过向知识库注入伪造文档，使大语言模型将虚假信息作为权威事实输出。攻击成功需满足两个关键条件：伪造文档在语义上与目标查询高度相似，且能超越真实文档被优先检索。实验表明，仅需三份精心设计的伪造文档即可在本地环境中成功实施攻击，导致系统输出完全错误的财务信息。这种攻击具有持久性、隐蔽性和低门槛的特点，而嵌入异常检测被证明是最有效的防御策略。

DeepSeek is a leading Chinese open-source large language model series known for its free commercial use, excellent performance, and ultra-long context capabilities. Since its launch in 2023, it has be

This article provides a comprehensive technical guide to edge detection in computer vision, covering both traditional methods (Sobel, Canny, LoG) and deep learning approaches (HED, RCF, GAN-based, Transformer-based). It includes implementation details, code examples in PyTorch, and training considerations. 原文翻译：本文全面介绍了计算机视觉中的边缘检测技术路线，涵盖传统方法（Sobel、Canny、LoG）和深度学习方法（HED、RCF、基于GAN、基于Transformer），包括实现细节、PyTorch代码示例和训练注意事项。

Google Skills productized the solution to Prompt Bloat by introducing reusable, dynamically loaded expertise units called Skills, which unify agent workflows across Gemini Enterprise, Agents CLI, and AI Edge Gallery, turning Skills into a portable cognition container ("Docker for Prompts"). 原文翻译： Google Skills通过引入可复用、动态加载的专家知识单元（Skills）解决了提示膨胀问题，统一了Gemini Enterprise、Agents CLI和AI Edge Gallery三大平台的代理工作流，使Skills成为可移植的认知容器（“提示的Docker”）。