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所在平台: Udemy |
课程主页: https://www.udemy.com/course/understanding-prompt-engineering/
课程评论：没有评论
课程名称：理解提示工程 概述：本课程深入探讨了提示工程的原则、策略和最佳实践，这是塑造人工智能模型行为和性能的重要方面。《理解提示工程》是一门全面的课程，旨在为学习者提供有效生成和利用自然语言处理（NLP）和机器学习（ML）应用中的提示所需的知识和技能。 模块 1：提示工程导论 课程 1：提示工程基础 - 概述提示工程及其在NLP和ML中的重要性 - 提示方法的发展历史和演变 模块 2：提示的类型及其应用 课程 2：封闭式提示 - 理解和创建用于特定答案的提示 - 在问答系统中的应用 课程 3：开放式提示 - 为创造性响应设计提示 - 在语言生成模型中的应用 模块 3：有效提示的策略 课程 4：探测性提示 - 设计提示以揭示模型偏见 - 使用探测性提示的伦理考虑 课程 5：对抗性提示 - 创建提示以进行模型的压力测试 - 通过对抗性提示增强模型的鲁棒性 模块 4：通过提示进行微调和优化 课程 6：使用提示进行模型微调 - 在模型训练过程中结合提示的技术 - 平衡提示的影响与模型的泛化能力 课程 7：优化提示选择 - 针对特定任务选择最优提示的方法 - 根据模型行为定制提示 模块 5：评估和偏见缓解 课程 8：提示性能评估 - 评估模型使用提示后的性能的指标和方法 - 解释和分析结果 课程 9：提示工程中的偏见缓解 - 识别和应对由提示引入的偏见的策略 - 确保提示模型的公平性和包容性 模块 6：实际应用和案例研究 课程 10：提示工程案例研究 - 探索成功实施和真实场景中的挑战 - 行业专家的客座讲座，分享他们的经验 这门课程提供了一个全面的学习平台，帮助学习者掌握提示工程的关键概念和实际应用技能。
This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance. Understanding Prompt Engineering is a comprehensive course designed to equip learners with the knowledge and skills to effectively generate and utilize prompts in natural language processing (NLP) and machine learning (ML) applications. This course delves into prompt engineering principles, strategies, and best practices, a crucial aspect in shaping AI models' behaviour and performance.Module 1: Introduction to Prompt EngineeringLesson 1: Foundations of Prompt EngineeringOverview of prompt engineering and its significance in NLP and ML.Historical context and evolution of prompt-based approaches.Module 2: Types of Prompts and Their ApplicationsLesson 2: Closed-Ended PromptsUnderstanding and creating prompts for specific answers.Applications in question-answering systems.Lesson 3: Open-Ended PromptsCrafting prompts for creative responses.Applications in language generation models.Module 3: Strategies for Effective PromptingLesson 4: Probing PromptsDesigning prompts to reveal model biases.Ethical considerations in using probing prompts.Lesson 5: Adversarial PromptsCreating prompts to stress-test models.Enhancing robustness through adversarial prompting.Module 4: Fine-Tuning and Optimizing with PromptsLesson 6: Fine-Tuning Models with PromptsTechniques for incorporating prompts during model training.Balancing prompt influence and generalization.Lesson 7: Optimizing Prompt SelectionMethods for selecting optimal prompts for specific tasks.Customizing prompts based on model behavior.Module 5: Evaluation and Bias MitigationLesson 8: Evaluating Prompt PerformanceMetrics and methodologies for assessing model performance with prompts.Interpreting and analyzing results.Lesson 9: Bias Mitigation in Prompt EngineeringStrategies to identify and address biases introduced by prompts.Ensuring fairness and inclusivity in prompt-based models.Module 6: Real-World Applications and Case StudiesLesson 10: Case Studies in Prompt EngineeringExploration of successful implementations and challenges in real-world scenarios.Guest lectures from industry experts sharing their experiences.
