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Chee Keong Eu

成为会员时间：2023

Agent Assist and its Gen AI Capabilities Earned Dec 19, 2024 EST
Webhook fundamentals Earned Dec 13, 2024 EST
Agent Summarization (Custom) Earned Dec 11, 2024 EST
Customer Engagement Suite with Google AI Architecture Earned Dec 11, 2024 EST
Conversational AI and its Engagement Framework Earned Dec 11, 2024 EST
Architecting and Installing the Apigee Hybrid API Platform Earned Dec 10, 2024 EST
生成式 AI 適用的機器學習運作 (MLOps) Earned Sep 24, 2024 EDT
使用 Gemini：端對端 SDLC Earned Aug 24, 2024 EDT
使用 Gemini：DevOps 工程師 Earned Aug 20, 2024 EDT
使用 Gemini：數據資料學家和分析師 Earned Aug 19, 2024 EDT
使用 Gemini：雲端架構師 Earned Aug 15, 2024 EDT
使用 Gemini：應用程式開發人員 Earned Aug 15, 2024 EDT
Vector Search 和嵌入 Earned Aug 10, 2024 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned May 24, 2024 EDT
在 Google Cloud 整合應用程式與 Gemini 1.0 Pro Earned May 22, 2024 EDT
Applying Machine Learning to your Data with Google Cloud Earned May 22, 2024 EDT
Detect Manufacturing Defects Using Visual Inspection AI Earned May 22, 2024 EDT
運用 BigQuery ML 建立機器學習模型 Earned May 22, 2024 EDT
Analyze Sentiment with Natural Language API Earned May 21, 2024 EDT
Analyze Speech and Language with Google APIs Earned May 21, 2024 EDT
Analyze Images with the Cloud Vision API Earned May 21, 2024 EDT
Build LookML Objects in Looker Earned May 7, 2024 EDT
Architecting with Google Kubernetes Engine: Foundations - 繁體中文 Earned Oct 28, 2023 EDT
API Design and Fundamentals of Google Cloud's Apigee API Platform Earned Oct 28, 2023 EDT
Production Machine Learning Systems Earned Oct 25, 2023 EDT
Machine Learning Operations (MLOps): Getting Started Earned Oct 23, 2023 EDT
Natural Language Processing on Google Cloud Earned Oct 23, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Oct 20, 2023 EDT
Launching into Machine Learning Earned Oct 19, 2023 EDT
Google Cloud 的 AI 和機器學習服務簡介 Earned Oct 18, 2023 EDT
探索生成式 AI - Vertex AI Earned Oct 16, 2023 EDT
Vertex AI Studio 簡介 Earned Oct 11, 2023 EDT
建立圖像說明生成模型 Earned Oct 11, 2023 EDT
Transformer 和 BERT 模型 Earned Oct 10, 2023 EDT
編碼器-解碼器架構 Earned Oct 10, 2023 EDT
注意力機制 Earned Oct 10, 2023 EDT
圖像生成簡介 Earned Oct 9, 2023 EDT
Virtual Agent Development in Dialogflow CX for Citizen Devs Earned Oct 9, 2023 EDT
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則 Earned Oct 9, 2023 EDT
Generative AI Fundamentals - 繁體中文 Earned Oct 9, 2023 EDT
負責任的 AI 技術簡介 Earned Oct 9, 2023 EDT
大型語言模型簡介 Earned Oct 9, 2023 EDT
生成式 AI 簡介 Earned Oct 8, 2023 EDT
Virtual Agent Development in Dialogflow ES for Software Devs Earned Oct 5, 2023 EDT
Virtual Agent Development in Dialogflow ES for Citizen Devs Earned Oct 4, 2023 EDT
Contact Center AI: Conversational Design Fundamentals Earned Oct 2, 2023 EDT

Unlock the power of generative AI to create intelligent, automated agents. After completing this course, you'll be equipped to develop a data store agent that can instantly answer complex questions by automatically extracting and synthesizing information from your websites, documents, or structured data. Say goodbye to static FAQs—your new agent will provide dynamic, accurate answers and even surface the original source URLs, all with a simple and rapid setup.

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In this course, you will learn the important role that different types of webhooks play in Dialogflow CX development, and how to effectively integrate them into your routine configuration of a Virtual Agent.

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In this course you will learn how Contact Center AI Agent Assist can help distill complex customer interactions into concise and clear summaries.

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In this course you will learn the key architectural considerations that need to be taken into account when designing for the implementation of Conversational AI solutions. Please note Dialogflow CX was recently renamed to Conversational Agents and CCAI Insights was renamed to Conversational Insights.

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Transform your understanding of customer service with this course on the Customer Engagement Suite (CES) and its powerful generative AI capabilities. You'll start by tracing the journey of contact centers, understanding how they've evolved and where gen AI is propelling them next. Then, you'll gain a deep understanding of the core building blocks within the CES solution, seeing how each component contributes to delivering exceptional customer experiences. The course concludes by exploring the robust business case for CES, along with practical use cases and the various user personas that benefit from this innovative solution.

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This course introduces you to the fundamentals and practices used to install and manage Google Cloud's Apigee API Platform for hybrid cloud. Through a combination of lectures, a hands-on lab, and supplemental materials, you will learn how to install and operate the Apigee API Platform.

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本課程旨在提供必要的知識和工具，協助您探索機器學習運作團隊在部署及管理生成式 AI 模型時面臨的獨特挑戰，並瞭解 Vertex AI 如何幫 AI 團隊簡化機器學習運作程序，打造成效非凡的生成式 AI 專案。

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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助您透過 Google Cloud 使用 Google 產品和服務，開發、測試、部署及管理應用程式。有了 Gemini 的協助，您會學到如何開發和建構網頁應用程式、修正應用程式中的錯誤、開發測試及查詢資料。在實作研究室中，您也會體驗到 Gemini 如何改良軟體開發生命週期 (SDLC)。 Duet AI 已更名為 Gemini，這是我們的新一代模型。

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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助工程師透過 Google Cloud 管理基礎架構。您將學到如何透過提示讓 Gemini 尋找和瞭解應用程式記錄檔、建立 GKE 叢集，以及研究如何打造建構環境。在實作研究室中，您也會瞭解 Gemini 如何改良開發運作的工作流程。 Duet AI 已更名為 Gemini，是我們新一代的模型。

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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助分析客戶資料及預測產品銷售情形。您也會學習如何在 BigQuery 中使用客戶資料識別、分類及開發新客戶。透過使用實作研究室，您可以體驗 Gemini 如何改良資料分析和機器學習工作流程。 Duet AI 已更名為 Gemini，這是我們的新一代模型。

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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助管理員在 Google Cloud 佈建基礎架構。您將瞭解如何透過提示讓 Gemini 解釋基礎架構、部署 GKE 叢集，以及更新既有的基礎架構。在實作研究室中，您也會體驗到 Gemini 如何改良 GKE 的部署工作流程。 Duet AI 已更名為 Gemini，這是我們的新一代模型。

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本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助開發人員透過 Google Cloud 建構應用程式。您將瞭解如何透過提示讓 Gemini 為您解釋程式碼內容、推薦 Google Cloud 服務，以及生成應用程式的程式碼。在實作研究室中，您也會體驗到 Gemini 如何改良應用程式的開發工作流程。 Duet AI 已更名為 Gemini，這是我們的新一代模型。

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這堂課程會介紹 AI 搜尋技術、工具和應用程式。主題涵蓋使用向量嵌入執行語意搜尋；結合語意和關鍵字做法的混合型搜尋機制；以及運用檢索增強生成 (RAG) 技術建構有基準的 AI 代理，盡可能減少 AI 幻覺。您可以實際使用 Vertex AI Vector Search，打造智慧型搜尋引擎。

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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

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這個簡短的課程會說明如何在 Google Cloud 整合應用程式與 Gemini 1.0 Pro 模型，讓您瞭解 Gemini API 及其生成式 AI 模型，並學習如何透過程式碼存取 Gemini 1.0 Pro 和 Gemini 1.0 Pro Vision 模型。另外，您會在應用程式中使用文字、圖片和影片提示，測試模型的功能。

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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

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Earn a skill badge by completing the Detect Manufacturing Defects using Visual Inspection AI course, where you learn how to use Visual Inspection AI to deploy a solution artifact and test that it can successfully identify defects in a manufacturing process.

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完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程，即可證明您具備下列技能： 可使用 BigQuery ML 建立及評估機器學習模型，並根據資料進行預測。 「技能徽章」是 Google Cloud 核發的獨家數位徽章， 用於肯定您在 Google Cloud 產品和服務方面的精熟技能， 代表您已通過測驗，能在互動式實作環境中應用相關知識。完成 本課程及結業評量挑戰實驗室，即可取得技能徽章 並與他人分享。

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Earn a skill badge by completing the Analyze Sentiment with Natural Language API quest, where you learn how the API derives sentiment from text.

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Earn a skill badge by completing the Analyze Speech and Language with Google APIs quest, where you learn how to use the Natural Language and Speech APIs in real-world settings.

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Earn a skill badge by completing the Analyze Images with the Cloud Vision API quest, where you discover how to leverage the Cloud Vision API for various tasks, including extracting text from images.

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Complete the introductory Build LookML Objects in Looker skill badge course to demonstrate skills in the following: building new dimensions and measures, views, and derived tables; setting measure filters and types based on requirements; updating dimensions and measures; building and refining Explores; joining views to existing Explores; and deciding which LookML objects to create based on business requirements.

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在 「Google Kubernetes Engine 架構：基礎知識」的課程中，您將復習 Google Cloud 的配置和原則，接著是建立和管理軟體容器簡介和 Kubernetes 架構簡介。 這是 Google Kubernetes Engine 架構系列中的第一項課程。完成此課程後，請註冊 Google Kubernetes Engine 架構：工作負載課程。

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In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API life cycle, and how the Apigee API platform helps you manage all aspects of the life cycle. You learn about how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with Google Cloud's Apigee API Platform series. After completing this course, enroll in the API Security on Google Cloud's Apigee API Platform course.

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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

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本課程介紹 Google Cloud 中的 AI 和機器學習 (ML) 服務。這些服務可建構預測式和生成式 AI 專案。我們將帶您探索「從資料到 AI」生命週期中適用的技術、產品和工具，包括 AI 基礎、開發選項及解決方案。課程目的是藉由生動的學習體驗與實作練習，增進數據資料學家、AI 開發人員和機器學習工程師的技能與知識。

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探索生成式 AI - Vertex AI 課程包含一系列實驗室，幫助您瞭解 如何在 Google Cloud 使用生成式 AI。透過實驗室，您將瞭解 如何使用 Vertex AI PaLM API 系列模型，包括 text-bison、chat-bison、 和 textembedding-gecko。您也會瞭解提示設計、最佳做法、 以及這些模型如何用於構思、文字分類、文字擷取、文字 摘要等。您也會瞭解如何透過 Vertex AI 自訂訓練功能調整基礎模型， 並將模型部署至 Vertex AI 端點。

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本課程會介紹 Vertex AI Studio。您可以運用這項工具和生成式 AI 模型互動、根據商業構想設計原型，並投入到正式環境。透過身歷其境的應用實例、有趣的課程及實作實驗室，您將能探索從提示到正式環境的生命週期，同時學習如何將 Vertex AI Studio 運用在多模態版 Gemini 應用程式、提示設計、提示工程和模型調整。這個課程的目標是讓您能運用 Vertex AI Studio，在專案中發揮生成式 AI 的潛能。

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本課程說明如何使用深度學習來建立圖像說明生成模型。您將學習圖像說明生成模型的各個不同組成部分，例如編碼器和解碼器，以及如何訓練和評估模型。在本課程結束時，您將能建立自己的圖像說明生成模型，並使用模型產生圖像說明文字。

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這堂課程將說明變換器架構，以及基於變換器的雙向編碼器表示技術 (BERT) 模型，同時帶您瞭解變換器架構的主要組成 (如自我注意力機制) 和如何用架構建立 BERT 模型。此外，也會介紹 BERT 適用的各種任務，像是文字分類、問題回答和自然語言推論。課程預計約 45 分鐘。

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本課程概要說明解碼器與編碼器的架構，這種強大且常見的機器學習架構適用於序列對序列的任務，例如機器翻譯、文字摘要和回答問題。您將認識編碼器與解碼器架構的主要元件，並瞭解如何訓練及提供這些模型。在對應的研究室逐步操作說明中，您將學習如何從頭開始使用 TensorFlow 寫程式，導入簡單的編碼器與解碼器架構來產生詩詞。

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本課程將介紹注意力機制，說明這項強大技術如何讓類神經網路專注於輸入序列的特定部分。此外，也將解釋注意力的運作方式，以及如何使用注意力來提高各種機器學習任務的成效，包括機器翻譯、文字摘要和回答問題。

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本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。

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Welcome to "Virtual Agent Development in Dialogflow CX for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). In this course, you'll be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations using Dialogflow CX.

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隨著企業持續擴大使用人工智慧和機器學習，以負責任的方式發展相關技術也日益重要。對許多企業來說，談論負責任的 AI 技術可能不難，如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術，本課程絕對能助您一臂之力。 您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談，協助貴機構奠定良好基礎，實踐負責任的 AI 技術。

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完成「Introduction to Generative AI」、「Introduction to Large Language Models」和「Introduction to Responsible AI」課程，即可獲得技能徽章。通過最終測驗，就能展現您對生成式 AI 基本概念的掌握程度。 「技能徽章」是 Google Cloud 核發的數位徽章，用於表彰您對 Google Cloud 產品和服務的相關知識。您可以將技能徽章公布在社群媒體的個人資料中，向其他人分享您的成果。

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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。

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這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。

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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。

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Welcome to "CCAI Virtual Agent Development in Dialogflow ES for Software Developers", the third course in the "Customer Experiences with Contact Center AI" series. In this course, learn to use additional features of Dialogflow ES for your virtual agent, create a Firestore instance to store customer data, and implement cloud functions that access the data. With the ability to read and write customer data, learner’s virtual agents are conversationally dynamic and able to defer contact center volume from human agents. You'll be introduced to methods for testing your virtual agent and logs which can be useful for understanding issues that arise. Lastly, learn about connectivity protocols, APIs, and platforms for integrating your virtual agent with services already established for your business.

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Welcome to "Virtual Agent Development in Dialogflow ES for Citizen Devs", the second course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to develop customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will use Dialogflow ES to create virtual agents and test them using the Dialogflow ES simulator. This course also provides best practices on developing virtual agents. You will also be introduced to adding voice (telephony) as a communication channel to your virtual agent conversations. Through a combination of presentations, demos, and hands-on labs, participants learn how to create virtual agents. This is an intermediate course, intended for learners with the following types of roles: Conversational designers: Designs the user experience of a virtual assistant. Translates the brand's business requirements into natural dialog flows. Citizen developers: Creates new business applications fo…

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Welcome to "CCAI Conversational Design Fundamentals", the first course in the "Customer Experiences with Contact Center AI" series. In this course, learn how to design customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You will be introduced to CCAI and its three pillars (Dialogflow, Agent Assist, and Insights), and the concepts behind conversational experiences and how the study of them influences the design of your virtual agent. After taking this course you will be prepared to take your virtual agent design to the next level of intelligent conversation.

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