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所在平台: Udemy |
课程主页: https://www.udemy.com/course/genai-world-llm-fine-tuning-rag-prompt-engineering/
课程评论：没有评论
课程名称：生成式人工智能：大型语言模型（LLM）、微调、检索增强生成（RAG）与提示工程 课程概述：本课程涵盖了从大型语言模型（LLMs）和提示工程到微调的各个方面，以及更高级的概念，如直接偏好优化（DPO）。学员将深入了解检索增强生成（RAG），该技术通过整合检索系统增强LLM的能力，以提供更准确和优越的响应。课程结束时，学员将能够创建与人类意图完美契合且超越标准模型的人工智能解决方案。 课程内容：除了核心主题外，本课程还包括关于微调、提示工程和检索增强生成（RAG）的深入实际案例研究。这些案例研究不仅凸显了前沿技术，还提供了实际项目中的应用洞察。通过探索真实场景和项目，学员将深入理解如何有效利用这些方法解决复杂的挑战。案例研究旨在弥合理论与实践之间的差距，使参与者看到这些先进技术在行业中的应用。 此外，这些示例提供了将理论概念应用于实际应用的逐步框架。无论是为提高性能而微调模型、构建有效的提示以改善输出，还是利用检索系统增强生成，学员都能自信地在自己的项目中实施这些策略。确保在课程结束时，参与者不仅具备生成式人工智能概念的坚实基础，还能以实际和有影响力的方式应用这些知识。
This course covers everything from Large Language Models (LLMs) and prompt engineering to fine-tuning , as well as advanced concepts like Direct Preference Optimization (DPO). You'll also dive deep into Retrieval-Augmented Generation (RAG), which enhances your LLMs' capabilities by integrating retrieval systems for more accurate and superior responses.By the end of this course, you'll be equipped to create AI solutions that align perfectly with human intent and outperform standard models.What You Will GetIn addition to the core topics, our course features in-depth, real-world case studies on fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG). These case studies not only highlight cutting-edge techniques but also offer practical, hands-on insights into their application in real-world AI projects. By exploring actual scenarios and projects, learners will gain a deep understanding of how to effectively utilize these methods to solve complex challenges. The case studies are designed to bridge the gap between theory and practice, enabling participants to see how these advanced techniques are deployed in industry settings.Moreover, these examples provide a step-by-step framework for applying theoretical concepts to real-world applications. Whether it's fine-tuning models for enhanced performance, engineering prompts for improved outputs, or leveraging retrieval systems to augment generation, learners will be able to confidently implement these strategies in their own projects. This ensures that by the end of the course, participants will not only have a solid foundation in generative AI concepts but also the ability to apply them in practical, impactful ways.
