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所在平台: Udemy |
课程主页: https://www.udemy.com/course/llm-apps-prototyping-model-evaluation-and-improvements/
课程评论：没有评论
课程名称：LLM 应用：原型开发、模型评估与改进 课程概述：本课程旨在帮助学员全面了解大语言模型（LLMs）的潜力，通过原型开发、模型评估和基准测试。本实操课程涵盖了 LLM 开发的每个阶段，包括模型的构建与选择、微调、测试以及使用行业标准工具进行基准测试。无论是 AI 新手还是希望提升专业技能的从业人员，本课程都提供了创造高效能 AI 应用所需的技能。 课程内容： 1. 设置 AI 开发环境：学习如何准备强大的 AI 工作空间，包括 Python、VS Code、NPM 和必要的 AI 库，以确保流畅的开发体验。 2. 理解 AI 和机器学习基础：探讨 AI、机器学习和深度学习的关键概念，包括监督学习与无监督学习、模型训练阶段，以及 LLM 如何处理和生成响应。 3. 选择适合您用例的 AI 模型：了解如何为自然语言处理、视觉及多模态应用选择最佳的预训练 AI 模型，学习分类、聚类和回归模型的使用时机，并理解模型复杂性、速度和准确性之间的权衡。 4. 利用检索增强生成（RAG）：通过结合基于检索的搜索与 LLM 响应，加强您的 AI 应用，提供更准确和上下文意识的 AI 输出。 5. 利用 Hugging Face AI 社区：探索 Hugging Face 生态系统，了解模型库、分词器和转换器，并为开源 AI 运动做出贡献。 6. 微调模型以实现最大性能：尝试温度设置、top-K 和 top-P 抽样以及超参数调优，以优化 LLM 的响应和效率。 7. 通过数据驱动的洞察来提升 AI：利用 K 折交叉验证提高模型准确性，学习有效的数据拆分技术，并探索过拟合和欠拟合的检测方法。 8. 像专业人士一样对 AI 模型进行基准测试：将模型与 GLUE 和 Hugging Face 领导者榜单等行业基准进行比较，学习如何使用标准指标评估 NLP 模型，并使用 Python 进行实际的 GLUE 基准测试。 9. 评估计算机视觉 AI 模型：学习如何使用 CIFAR-10 基准测试视觉基础的 AI 模型，并解读测试结果以实现高级模型评估。 10. 理解混淆矩阵的模型评估：掌握混淆矩阵分析，以评估分类模型性能，学习解释真实正例、假阳性、假阴性和真实负例，从而优化 AI 预测和减少错误。 适合人群： - 渴望深入了解 LLM 原型开发与评估的 AI 爱好者 - 希望构建和完善先进 AI 模型的开发者 - 希望自信评估 AI 性能的数据科学家 - 对 AI 模型评估技术感兴趣的任何人 通过本课程，您将获得构建和优化高性能 AI 应用所需的全面技能和知识。
Unlock the full potential of Large Language Models (LLMs) by understanding prototyping, model evaluation, and benchmarking. This hands-on course takes you through every stage of LLM development-from building and selecting models to fine-tuning, testing, and benchmarking them with industry-standard tools. Whether you're an AI beginner or a professional looking to enhance your expertise, this course provides the skills needed to create high-performing AI applications.What You'll Learn: Set Up Your AI Development EnvironmentLearn how to prepare a powerful AI workspace with Python, VS Code, NPM, and essential AI libraries, ensuring a seamless development experience.Understand AI & Machine Learning BasicsExplore key concepts in AI, Machine Learning, and Deep Learning, including supervised vs. unsupervised learning, model training phases, and how LLMs process and generate responses.Selecting the Right AI Model for Your Use CaseDiscover how to choose the best pre-trained AI models for NLP, vision, and multi-modal applications. Learn when to use classification, clustering, and regression models and understand model complexity, speed, and accuracy trade-offs. Harness the Power of Retrieval-Augmented Generation (RAG)Enhance your AI applications with RAG, a technique that combines retrieval-based search with LLM responses for more accurate and context-aware AI outputs.Leverage the Hugging Face AI CommunityTap into the Hugging Face ecosystem-explore model repositories, learn about tokenizers and transformers, and contribute to the open-source AI movement.Fine-Tune Models for Maximum PerformanceExperiment with temperature settings, top-K and top-P sampling, and hyperparameter tuning to optimize LLM responses and efficiency.Supercharge Your AI with Data-Driven InsightsImprove model accuracy with K-Fold Cross Validation, learn effective data-splitting techniques, and explore overfitting and underfitting detection methods.Benchmark Your AI Models Like a ProCompare your models against industry benchmarks like GLUE and Hugging Face Leaderboards. Learn how to evaluate NLP models using standard metrics and perform real-world GLUE benchmarking with Python.Evaluate Computer Vision AI ModelsGo beyond text-based models! Learn how to benchmark vision-based AI models using CIFAR-10 and interpret test results for advanced model evaluation. Understand Model Evaluation with Confusion MatricesMaster Confusion Matrix analysis to assess classification model performance. Learn how to interpret True Positives, False Positives, False Negatives, and True Negatives to optimize AI predictions and reduce errors.Who Should Take This Course? AI enthusiasts eager to dive into LLM prototyping and evaluation Developers looking to build and refine state-of-the-art AI models Data scientists who want to benchmark AI performance with confidence Anyone interested in understanding AI model evaluation techniques
