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所在平台: Udemy |
课程主页: https://www.udemy.com/course/amazon-bedrock-learn-ai-on-aws-with-python/
课程评论：没有评论
课程名称：Amazon Bedrock - 学习使用Python在AWS上进行AI开发！ 课程概述：欢迎参加本课程，深入了解亚马逊的AI平台——Bedrock。本课程精心设计，旨在让学员体验到Bedrock的实用Python应用，适合AI初学者及有经验的从业者。本课程将深化您的理解并提供动手实践的机会。 课程内容： 第一部分提供课程布局、必要资源及常见问题的介绍，是您AI冒险的基础，包括关于Amazon Bedrock的安装和设置指引。第二部分探讨Amazon Bedrock的文本模型的复杂性，您将学习关键的文本处理参数，并与Amazon Titan和Llama 2等先进文本建模工具合作，包含分析电话记录的项目和提取、处理PDF信息的练习。 第三部分引入AI驱动的图像生成世界，涵盖使用Stability AI参数和Amazon的Boto3进行图像创作的要点，重点是Recipe Code Along项目，将创造性和技术性地生成可视化食谱指南。 最后第四部分聚焦于检索增强生成（RAG），这一高级主题在AI中至关重要。您将学习RAG的实际应用及其好处，特别是Amazon Bedrock如何将嵌入技术和大型语言模型集成在RAG中。 通过本课程，您将掌握从文本处理到图像生成及检索增强生成的一系列实用技能，为您的AI旅程奠定坚实基础。
Welcome to this course on Amazon Bedrock. This program has been expertly crafted to immerse you in the world of Amazon's AI platform, Bedrock, emphasizing practical Python applications. Suitable for both AI novices and seasoned practitioners, this course promises to deepen your understanding and provide hands-on experience in AI.The course journey commences with an enlightening Section 1, offering an introduction to the course layout, essential resources, and FAQs. This foundational segment is essential for equipping you with the necessary tools and knowledge about Amazon Bedrock, including detailed installation and setup instructions to kickstart your AI adventure.In Section 2, we delve into the complexities of Amazon Bedrock's text models. You'll explore critical text processing parameters and work with Amazon Titan and Llama 2, Bedrock's advanced text modeling tools. This section combines theoretical knowledge with practical application, featuring a project on call transcript analysis and exercises to enhance your skills in extracting and processing information from PDFs.Section 3 transports you to the fascinating world of AI-powered image generation. It covers the essentials of image creation with Stability AI parameters and Amazon's Boto3, including Titan's capabilities in this area. The section's highlight is the Recipe Code Along Project, where you will creatively and technically generate a visual recipe guide.The course culminates in Section 4, focusing on Retrieval Augmented Generation (RAG). This advanced topic is pivotal in AI, and you'll learn about its practical applications and benefits, particularly how Amazon Bedrock integrates embeddings and large language models in RAG.
