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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-testing-ai-models-llms-and-chatbots/
课程评论：没有评论
课程名称：人工智能模型测试大师班 课程概述：《人工智能测试：基础模型、大语言模型、聊天机器人及更多》是您了解测试先进人工智能系统基础知识的全面指南。无论您是开发者、数据科学家，还是仅仅对人工智能感兴趣的爱好者，本课程将为您提供必要的知识和技能，以评估和提升人工智能技术的可靠性、性能和安全性。 您将学习的内容包括： - **人工智能测试导论**：理解测试人工智能系统的重要性，包括伦理考量和人工智能失败的潜在影响。 - **测试基础**：学习不同类型的测试方法论，如单元测试、集成测试和系统测试，并应用于人工智能。 - **基础模型和大语言模型的重点测试**：深入探讨测试大型语言模型和基础人工智能系统的挑战和技术，这些系统正在重塑众多行业。 - **聊天机器人测试**：探讨测试会话型人工智能的独特方面，确保其在各种场景中做出准确和恰当的回应。 - **人工智能系统评估**：学习如何为不同的基于人工智能的系统设计并实施有效的测试方案，使用手动和自动工具。 - **数据K折划分**：理解如何将数据划分为：训练、评估和测试数据。通过对数据进行划分和训练，每次使用不同的验证子集，确保模型从多个角度学习，从而提高泛化能力，减少过拟合，并提供更可靠的模型性能评估。 - **案例研究**：从现实场景中获得洞察，突出人工智能测试中的常见陷阱和最佳实践。 - **伦理人工智能**：理解人工智能中的风险及背后的伦理。如何测试这些内容。 - **基准测试**：了解如何将人工智能与一些常见基准模型进行测试，如：BLUE、HellaSWAG、MMLU、HumanEval等。 - **使用API测试ChatGPT/聊天机器人，并将其整合入MLOPS链**。 - **对抗性人工智能**：理解如何测试人工智能模型的鲁棒性。 本课程针对希望获得人工智能系统测试的基本技术和实践的人士，适合初涉人工智能领域的职业生涯、寻求提升专业技能或对人工智能系统可靠性机制感兴趣的学习者。 课程特点包括： - 引人入胜的视频讲座 - 实际作业和动手项目 - 知识测验和考试 - 讨论与合作的社区论坛 - 终身访问课程材料 立即注册，掌握测试人工智能系统的关键技能，为您贡献于安全可靠的人工智能技术发展做好准备！
Welcome to "Testing AI: Foundation Models, LLMs, Chatbots & More," your comprehensive guide to understanding the fundamentals of testing advanced AI systems. Whether you're a developer, a data scientist, or simply an AI enthusiast, this course will equip you with the knowledge and skills necessary to assess and improve the reliability, performance, and safety of AI technologies.What You Will Learn:Introduction to AI Testing: Understand the importance of testing AI systems, including ethical considerations and the potential impacts of AI failures.Testing Basics: Learn about different types of testing methodologies like unit testing, integration testing, and system testing as applied to AI.Special Focus on Foundation Models and LLMs: Dive deep into the challenges and techniques for testing large language models and foundational AI systems that are reshaping numerous industries.Chatbot Testing: Explore the unique aspects of testing conversational AI, ensuring they respond accurately and appropriately in varied scenarios.AI System Evaluations: Learn how to design and implement effective testing regimes for different AI-based systems, using both manual and automated tools.K - Folding of Data: Understand how to split all your data into: Training, Evaluation and testing data. Get more out of your data by splitting and training it on the same data, but with different validation subsets each time, ensuring that your model learns from multiple perspectives. This technique helps improve generalization, reduces overfitting, and provides a more reliable estimate of model performance.Case Studies: Gain insights from real-world scenarios that highlight common pitfalls and best practices in AI testing.Ethical AI: understand the risk with AI and the ethics behind AI. How can and should you test for thisBenchmarking: Understand how to test the AI against some common benchmarking models such as: BLUE, HellaSWAG, MMLU, HumanEval and other.Testing ChatGPT / Chatbots with the help of an API and integration this into MLOPS chain.Adversarial AI: understand how to test for robustness in AI ModelsWho This Course Is For:This course is ideal for anyone looking to gain a solid grounding in the techniques and practices essential for testing AI systems. Whether you're starting a career in AI, looking to enhance your professional skills, or interested in the mechanisms behind AI system reliability, this course has valuable insights for you.Course Features:Engaging video lecturesPractical assignments and hands-on projectsQuizzes and exams to test your knowledgeAccess to a community forum for discussion and collaborationLifetime access to course materialsEnroll now to start mastering the crucial skill of testing AI systems and ensure you're prepared to contribute to the development of safe and reliable AI technologies!
