Responsible AI: Applying AI Principles with Google Cloud
Earned mars 31, 2024 EDT
Introduction to Responsible AI
Earned mars 31, 2024 EDT
Transformer Models and BERT Model
Earned mars 27, 2024 EDT
Create Image Captioning Models
Earned mars 27, 2024 EDT
Introduction to Image Generation
Earned mars 27, 2024 EDT
Attention Mechanism
Earned mars 27, 2024 EDT
Introduction to Large Language Models
Earned mars 26, 2024 EDT
Introduction to Generative AI
Earned mars 26, 2024 EDT
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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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 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 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 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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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 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.
Responsible AI: Applying AI Principles with Google Cloud
Earned mars 31, 2024 EDT
Introduction to Responsible AI
Earned mars 31, 2024 EDT
Transformer Models and BERT Model
Earned mars 27, 2024 EDT
Create Image Captioning Models
Earned mars 27, 2024 EDT
Introduction to Image Generation
Earned mars 27, 2024 EDT
Attention Mechanism
Earned mars 27, 2024 EDT
Introduction to Large Language Models
Earned mars 26, 2024 EDT
Introduction to Generative AI
Earned mars 26, 2024 EDT