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Marcos Dal Maso

成为会员时间：2023

青铜联赛

400 积分
Generative AI Fundamentals - 繁體中文徽章 Generative AI Fundamentals - 繁體中文 Earned Sep 21, 2023 EDT
Introduction to Responsible AI - 繁體中文徽章 Introduction to Responsible AI - 繁體中文 Earned Jul 9, 2023 EDT
Feature Engineering徽章 Feature Engineering Earned Jul 9, 2023 EDT
TensorFlow on Google Cloud徽章 TensorFlow on Google Cloud Earned Jul 7, 2023 EDT
Launching into Machine Learning徽章 Launching into Machine Learning Earned Jul 6, 2023 EDT
How Google Does Machine Learning徽章 How Google Does Machine Learning Earned Jul 5, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals徽章 Google Cloud Big Data and Machine Learning Fundamentals Earned Jun 22, 2023 EDT
Introduction to Large Language Models - 繁體中文徽章 Introduction to Large Language Models - 繁體中文 Earned Jun 22, 2023 EDT
Introduction to Generative AI - 繁體中文徽章 Introduction to Generative AI - 繁體中文 Earned Jun 22, 2023 EDT

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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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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This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

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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 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 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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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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