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在 Google Cloud 控制台中运用您的技能

Sakthivel Shreedharan

成为会员时间：2023

黄金联赛

37755 积分
在 Vertex AI 上构建和部署机器学习解决方案 Earned Aug 1, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned Aug 1, 2024 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned Jul 31, 2024 EDT
Recommendation Systems on Google Cloud Earned Jul 4, 2024 EDT
Launching into Machine Learning Earned Jul 4, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Jul 4, 2024 EDT
Google Cloud 上的 AI 和机器学习简介 Earned Jul 1, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Jul 1, 2024 EDT
Scaling with Google Cloud Operations Earned Jan 27, 2024 EST
Trust and Security with Google Cloud Earned Jan 27, 2024 EST
Modernize Infrastructure and Applications with Google Cloud Earned Jan 27, 2024 EST
Innovating with Google Cloud Artificial Intelligence Earned Jan 26, 2024 EST
Exploring Data Transformation with Google Cloud Earned Jan 24, 2024 EST
Digital Transformation with Google Cloud Earned Jan 3, 2024 EST
Vertex AI Studio 简介 Earned Jan 3, 2024 EST
创建图片标注模型 Earned Jan 3, 2024 EST
Transformer 模型和 BERT 模型 Earned Jan 3, 2024 EST
注意力机制 Earned Jan 3, 2024 EST
编码器-解码器架构 Earned Jan 2, 2024 EST
图像生成简介 Earned Jan 2, 2024 EST
Generative AI Fundamentals - 简体中文 Earned Jan 2, 2024 EST
负责任的 AI 简介 Earned Jan 2, 2024 EST
大型语言模型简介 Earned Jan 2, 2024 EST
生成式 AI 简介 Earned Sep 24, 2023 EDT

完成在 Vertex AI 上构建和部署机器学习解决方案课程，赢取中级技能徽章。 在此课程中，您将了解如何使用 Google Cloud 的 Vertex AI Platform、AutoML 以及自定义训练服务来 训练、评估、调优、解释和部署机器学习模型。 此技能徽章课程的目标受众是专业的数据科学家和机器学习 工程师。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您对 Google Cloud 产品与服务的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成此技能徽章课程 和作为最终评估的实验室挑战赛，即可获得数字徽章， 在您的人际圈中炫出自己的技能。

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This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

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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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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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本课程介绍 Google Cloud 的 AI 和机器学习 (ML) 能力，重点讲解如何开发生成式和预测式 AI 项目。本课程将探讨“数据到 AI”全生命周期中的多种技术、产品和工具，并通过互动练习帮助数据科学家、AI 开发者和机器学习工程师提升专业能力。

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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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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task. Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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As organizations move their data and applications to the cloud, they must address new security challenges. The Trust and Security with Google Cloud course explores the basics of cloud security, the value of Google Cloud's multilayered approach to infrastructure security, and how Google earns and maintains customer trust in the cloud. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences. “Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible. Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.

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There's much excitement about cloud technology and digital transformation, but often many unanswered questions. For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin? If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.

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本课程介绍 Vertex AI Studio，这是一种用于与生成式 AI 模型交互、围绕业务创意进行原型设计并在生产环境中落地的工具。通过沉浸式应用场景、富有吸引力的课程和实操实验，您将探索从提示到产品的整个生命周期，了解如何将 Vertex AI Studio 用于多模态 Gemini 应用、提示设计、提示工程和模型调优。本课程的目的在于帮助您利用 Vertex AI Studio，在自己的项目中充分发掘生成式 AI 的潜力。

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本课程教您如何使用深度学习来创建图片标注模型。您将了解图片标注模型的不同组成部分，例如编码器和解码器，以及如何训练和评估模型。学完本课程，您将能够自行创建图片标注模型并用来生成图片说明。

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本课程向您介绍 Transformer 架构和 Bidirectional Encoder Representations from Transformers (BERT) 模型。您将了解 Transformer 架构的主要组成部分，例如自注意力机制，以及该架构如何用于构建 BERT 模型。您还将了解可以使用 BERT 的不同任务，例如文本分类、问答和自然语言推理。完成本课程估计需要大约 45 分钟。

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本课程将向您介绍注意力机制，这是一种强大的技术，可令神经网络专注于输入序列的特定部分。您将了解注意力的工作原理，以及如何使用它来提高各种机器学习任务的性能，包括机器翻译、文本摘要和问题解答。

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本课程简要介绍了编码器-解码器架构，这是一种功能强大且常见的机器学习架构，适用于机器翻译、文本摘要和问答等 sequence-to-sequence 任务。您将了解编码器-解码器架构的主要组成部分，以及如何训练和部署这些模型。在相应的实验演示中，您将在 TensorFlow 中从头编写简单的编码器-解码器架构实现代码，以用于诗歌生成。

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本课程向您介绍扩散模型。这类机器学习模型最近在图像生成领域展现出了巨大潜力。扩散模型的灵感来源于物理学，特别是热力学。过去几年内，扩散模型成为热门研究主题并在整个行业开始流行。Google Cloud 上许多先进的图像生成模型和工具都是以扩散模型为基础构建的。本课程向您介绍扩散模型背后的理论，以及如何在 Vertex AI 上训练和部署此类模型。

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完成 Introduction to Generative AI、Introduction to Large Language Models 和 Introduction to Responsible AI 三门课程，赢取技能徽章。通过最终测验，即表明您理解了生成式 AI 的基本概念。 技能徽章是由 Google Cloud 颁发的数字徽章，旨在认可您对 Google Cloud 产品与服务的了解程度。公开您的个人资料并将技能徽章添加到您的社交媒体个人资料中，以此来分享您获得的成就。

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这是一节入门级微课程，旨在解释什么是负责任的 AI、它的重要性，以及 Google 如何在自己的产品中实现负责任的 AI。此外，本课程还介绍了 Google 的 7 个 AI 开发原则。

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这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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