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Tom Axberg

成为会员时间：2022

黄金联赛

14820 积分
Reinforcement Learning with Human Feedback (RLHF) Earned Jul 4, 2024 EDT
Orchestrate LLM solutions with LangChain Earned Jun 28, 2024 EDT
Improving developer velocity with Gemini Code Assist Earned Jun 28, 2024 EDT
使用多模态 Gemini 和多模态 RAG 检查富文档 Earned Jun 26, 2024 EDT
Custom Search with Embeddings in Vertex AI Earned Jun 26, 2024 EDT
使用 Vertex AI 中的 Gemini API 探索生成式 AI Earned Jun 25, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning Earned Jun 25, 2024 EDT
矢量搜索和嵌入 Earned Jun 24, 2024 EDT
Build and Deploy a Generative AI solution using a RAG framework Earned Apr 12, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications Earned Apr 11, 2024 EDT
Text Prompt Engineering Techniques Earned Apr 8, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps Earned Apr 5, 2024 EDT
Feature Engineering Earned Jan 9, 2023 EST

RHLF is a technique for fine-tuning language models by incorporating human feedback into the training process. This course explores how you can use RHLF to improve the performance of language models on various tasks, such as text summarization and question answering.

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Learn to use LangChain to call Google Cloud LLMs and Generative AI Services and Datastores to simplify complex applications' code.

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Learn how Gemini can revolutionize your ability to develop applications! This course helps developers go beyond the basics and learn how to integrate Gemini into their workflows.

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完成中级技能徽章课程使用多模态 Gemini 和多模态 RAG 检查富文档，展示您在以下方面的技能： 将多模态与 Gemini 配合使用，从而使用多模态提示从文本数据和视觉数据中提取信息、生成视频说明、 检索视频中不包含的额外信息； 将多模态检索增强生成 (RAG) 与 Gemini 配合使用，以构建包含文本和图片的文档的元数据、获取所有相关文本块并输出引用。

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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search. You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.

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完成中级技能徽章课程使用 Vertex AI 中的 Gemini API 探索生成式 AI，展示自己在以下方面的技能： 文本生成技能、用于增强内容创作能力的图像和视频分析技能，以及在 Gemini API 中应用函数调用技术的技能。 了解如何运用先进的 Gemini 技术、探索多模态内容生成方法，并扩展 AI 赋能项目的功能。

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(This course was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.

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在本次课程中，探索 AI 赋能的搜索技术、工具和应用。学习利用向量嵌入的语义搜索、融合语义和关键字的混合搜索方法，以及检索增强生成 (RAG) 技术，以打造基于事实的 AI 智能体，尽可能减少 AI 幻觉。获取 Vertex AI Vector Search 实战经验，打造您自己的智能搜索引擎。

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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.

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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.

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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.

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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.

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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

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