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Omar Irfan Khan

成为会员时间:2021

黄金联赛

21305 积分
Attention Mechanism - 繁體中文 Earned Feb 1, 2025 EST
Introduction to Image Generation - 繁體中文 Earned Feb 1, 2025 EST
Responsible AI: Applying AI Principles with Google Cloud - 繁體中文 Earned Feb 1, 2025 EST
Gemini for Security Engineers - 繁體中文 Earned Jan 31, 2025 EST
Gemini for end-to-end SDLC - 繁體中文 Earned Jan 31, 2025 EST
Machine Learning Operations (MLOps) for Generative AI - 繁體中文 Earned Jan 30, 2025 EST
Create Engaging Video with Google Vids Earned Jan 30, 2025 EST
Gemini in Google Drive - 繁體中文 Earned Jan 30, 2025 EST
Gemini in Google Meet - 繁體中文 Earned Jan 30, 2025 EST
Gemini in Google Sheets - 繁體中文 Earned Jan 30, 2025 EST
Gemini in Google Slides - 繁體中文 Earned Jan 30, 2025 EST
Gemini in Google Docs - 繁體中文 Earned Jan 30, 2025 EST
Gemini in Gmail - 繁體中文 Earned Jan 30, 2025 EST
Introduction to Gemini for Google Workspace - 繁體中文 Earned Jan 30, 2025 EST
Serverless Data Processing with Dataflow: Develop Pipelines Earned Jan 30, 2025 EST
How Google Does Machine Learning Earned Oct 31, 2021 EDT
Preparing for your Professional Data Engineer Journey Earned Oct 25, 2021 EDT
Serverless Data Processing with Dataflow: Foundations Earned Oct 21, 2021 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Oct 20, 2021 EDT
Building Resilient Streaming Analytics Systems on Google Cloud Earned Oct 19, 2021 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud Earned Oct 18, 2021 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned Oct 14, 2021 EDT
API Development on Google Cloud's Apigee API Platform Earned Oct 12, 2021 EDT
API Security on Google Cloud's Apigee API Platform Earned Oct 12, 2021 EDT
API Design and Fundamentals of Google Cloud's Apigee API Platform Earned Oct 12, 2021 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Oct 12, 2021 EDT
在 Google Cloud 設定應用程式開發環境 Earned Oct 11, 2021 EDT
Implement Load Balancing on Compute Engine Earned Oct 8, 2021 EDT
Google Cloud Essentials Earned Oct 8, 2021 EDT

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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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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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 保護雲端環境和資源。您將學到如何將工作負載範例部署到 Google Cloud 中的環境,以及運用 Gemini 找出並修復安全性設定錯誤。在實作研究室中,您也會體驗到 Gemini 如何改良雲端安全防護機制。 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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In this course, you'll learn about Google Vids, an online video creation and editing app available to select Google Workspace users. Through lessons and demos, you'll learn how to build and tell compelling stories through video at work. You'll also discover how to seamlessly incorporate media, audio and video clips, customize styles, and easily share your creations. Some Vids features use generative AI to help you work more efficiently. Remember, generative AI tools including Gemini, may suggest inaccurate or inappropriate information. Don’t rely on Gemini features as medical, legal, financial or other professional advice. It’s also important to remember that the Gemini feature suggestions don’t represent Google’s views, and should not be attributed to Google.

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Gemini for Google Workspace 是一項外掛程式,可讓使用者存取生成式 AI 功能。本課程使用影片、實作活動和練習範例,深入介紹 Google 雲端硬盤中的 Gemini 的功能。 課程結束後,您將具備 Google 雲端硬盤中的 Gemini 的知識及技能,可自信地運用這項工具提升工作流程的效率。

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Gemini 版 Google Workspace 是一項外掛程式,可讓使用者存取生成式 AI 功能。本課程會使用影片、實作活動和練習範例,深入介紹 Gemini 版 Google Meet 的功能。您會學到如何透過 Gemini 生成背景圖片、提升視訊品質及翻譯字幕。本課程結束後,您將具備 Gemini 版 Google Meet 的知識及技能,安心運用這項工具提高視訊會議的效率。

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安裝 Gemini 版 Google Workspace 外掛程式後,客戶就能在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能,並說明如何在 Google 試算表善用這些功能,提高生產力和效率。

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Gemini 版 Google Workspace 是一項外掛程式,可讓客戶在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能,並說明如何在 Google 簡報善用這些功能,提高生產力和效率。

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使用者將能透過 Gemini 版 Google Workspace 外掛程式運用生成式 AI 功能。本課程會使用影片、實作活動和練習範例,深入介紹 Gemini 版 Google 文件的功能。您將學到如何透過 Gemini 使用提示生成撰寫內容、編輯寫好的文字,以提升整體工作效率。本課程結束後,您將具備 Gemini 版 Google 文件的知識及技能,可自信地運用這項工具提升寫作品質。

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Gemini 版 Google Workspace 是一項外掛程式,可讓客戶在 Google Workspace 使用生成式 AI 功能。這堂迷你課程會介紹 Gemini 的主要功能,並說明如何在 Gmail 善用這些功能,提高生產力和效率。

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客戶能透過 Gemini 版 Google Workspace 外掛程式在 Google Workspace 使用生成式 AI 功能。本學習路徑會介紹 Gemini 的主要功能,並說明如何在 Google Workspace 善用這些功能,提高生產力和效率。

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models. The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.

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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.

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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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完成 Engineer Data for Predictive Modeling with BigQuery ML 技能徽章中階課程, 即可證明您具備下列技能:運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換管道、 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 的工作負載、 運用 BigQuery ML 建構機器學習模型,以及使用 Cloud Composer 複製多個位置的資料。「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通程度, 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研究室,即可獲得技能徽章 並與親友分享。

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In this course, you learn how to create APIs that utilize multiple services and how you can use custom code on Apigee. You will also learn about fault handling, and how to share logic between proxies. You learn about traffic management and caching. You also create a developer portal, and publish your API to the portal. You learn about logging and analytics, as well as CI/CD and the different deployment models supported by Apigee. 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 third and final course of the Developing APIs with Google Cloud's Apigee API Platform course series.

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In this course, you learn how to secure your APIs. You explore the security concerns you will encounter for your APIs. You learn about OAuth, the primary authorization method for REST APIs. You will learn about JSON Web Tokens (JWTs) and federated security. You also learn about securing against malicious requests, safely sending requests across a public network, and how to secure your data for users of Apigee. 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 second course of the Developing APIs with Google Cloud's Apigee API Platform series. After completing this course, enroll in the API Development on Google Cloud's Apigee API Platform course.

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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 introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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只要修完「在 Google Cloud 設定應用程式開發環境」課程,就能獲得技能徽章。 在本課程中,您將學會如何使用以下技術的基本功能,建構和連結以儲存空間為中心的雲端基礎架構:Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。 「技能徽章」是 Google Cloud 核發的獨家數位徽章,用於表彰您相當熟悉 Google Cloud 產品與服務,並已通過測驗,能在互動式實作環境中應用相關知識。只要完成這個技能徽章課程和最終評量挑戰研究室,即可取得技能徽章並與親友分享成就。

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完成 Implement Load Balancing on Compute Engine 技能徽章入門課程,即可證明您具備下列技能: 編寫 gcloud 指令和使用 Cloud Shell、在 Compute Engine 建立及部署虛擬機器, 以及設定網路和 HTTP 負載平衡器。 「技能徽章」是 Google Cloud 核發的 獨家數位徽章,用於肯定您在 Google Cloud 產品與服務方面的精通程度, 代表您已通過測驗,能在互動式實作環境中應用相關 知識。完成這個課程及挑戰研究室 最終評量,即可取得技能徽章並與親友分享。

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In this introductory-level course, you get hands-on practice with the Google Cloud’s fundamental tools and services. Optional videos are provided to provide more context and review for the concepts covered in the labs. Google Cloud Essentials is a recommendeded first course for the Google Cloud learner - you can come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.

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