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Claudio Zito

Member since 2021

Gold League

8550 points
Badge for Generative AI Explorer - Vertex AI Generative AI Explorer - Vertex AI Earned Nov 2, 2023 EDT
Badge for Introduction to Vertex AI Studio Introduction to Vertex AI Studio Earned Nov 2, 2023 EDT
Badge for Create Image Captioning Models Create Image Captioning Models Earned Nov 2, 2023 EDT
Badge for Transformer Models and BERT Model Transformer Models and BERT Model Earned Nov 1, 2023 EDT
Badge for Encoder-Decoder Architecture Encoder-Decoder Architecture Earned Oct 24, 2023 EDT
Badge for Attention Mechanism Attention Mechanism Earned Oct 24, 2023 EDT
Badge for Introduction to Image Generation Introduction to Image Generation Earned Oct 24, 2023 EDT
Badge for Responsible AI: Applying AI Principles with Google Cloud Responsible AI: Applying AI Principles with Google Cloud Earned Oct 18, 2023 EDT
Badge for Generative AI Fundamentals Generative AI Fundamentals Earned Oct 18, 2023 EDT
Badge for Introduction to Responsible AI Introduction to Responsible AI Earned Oct 18, 2023 EDT
Badge for Introduction to Large Language Models Introduction to Large Language Models Earned Oct 10, 2023 EDT
Badge for Introduction to Generative AI Introduction to Generative AI Earned Oct 10, 2023 EDT
Badge for Implement Load Balancing on Compute Engine Implement Load Balancing on Compute Engine Earned Apr 13, 2021 EDT
Badge for Google Cloud Essentials Google Cloud Essentials Earned Apr 13, 2021 EDT

The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.

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This course introduces Vertex AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models so you can use their capabilities in your applications. In this course, you learn what Vertex AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, you will have a quiz to test your knowledge.

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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images

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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.

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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.

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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you. In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.

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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles.

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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.

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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.

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Complete the introductory Implement Load Balancing on Compute Engine skill badge to demonstrate skills in the following: writing gcloud commands and using Cloud Shell, creating and deploying virtual machines in Compute Engine, and configuring network and HTTP load balancers. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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In this introductory-level Quest, you will get hands-on practice with the Google Cloud’s fundamental tools and services. Google Cloud Essentials is the recommended first Quest for the Google Cloud learner - you will 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. 1-minute videos walk you through key concepts for each lab.

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