Google AI Courses
What You Need to Know
Introduction to Generative AI
Google’s Introduction to Generative AI is a microlearning course that provides an introduction to Generative AI, how it works, and how it differs from traditional machine learning methods. The course is designed to help learners understand the basics of Generative AI and how it can be used to develop Gen AI apps. The course covers the following topics:
- Define Generative AI
- Explain how Generative AI works
- Describe Generative AI Model Types
- Describe Generative AI Applications
The course is estimated to take approximately 45 minutes to complete, and learners can earn a badge of completion upon finishing the required items in the course. The course is available in English and is aimed at a general audience.
Introduction to Large Language Models
Google’s “Introduction to Large Language Models” is a concise microlearning course that introduces learners to the world of large language models (LLMs). It delves into the fundamental concepts surrounding these models, including their definition, utilization across various domains, and the application of prompt tuning to optimize their performance.
The primary aim of this course is to provide a foundational understanding of LLMs and how they can be harnessed in the development of Generation AI (Gen AI) applications. The course content covers several critical areas:
Learners are then introduced to the concept of large language models, demystifying how they differ from traditional models and tracing the historical evolution of LLMs.
The course explores real-world use cases of LLMs, demonstrating their prowess in text generation, language translation, and question-answering tasks.
- Prompt tuning, a key technique for enhancing LLM performance, is explained in detail.
- In addition, the course touches upon Google’s Gen AI development tools, providing learners with insights into the tools they can employ for creating Gen AI applications.
Estimating an approximate completion time of 45 minutes, the course rewards learners with a completion badge upon finishing its requirements. It is available in English and caters to a broad audience.
Further, Google Cloud extends its commitment to the LLM domain by offering an array of resources and tools. These include the What-If Tool, Model Cards, and Explainable AI, all designed to support the development and understanding of LLMs. Google Cloud further offers a learning path that combines hands-on labs and courses tailored to developers in the Gen AI field. This learning journey starts with introductory training and covers topics like Responsible AI for Developers, Introduction to Image Generation, and Attention Mechanisms.
Google’s “Introduction to Large Language Models” serves as an excellent entry point for those eager to acquaint themselves with LLMs and their versatile applications. It lays a solid foundation for further exploration and is presented in a manner accessible to a general audience.
Introduction to Responsible AI
Google’s Introduction to Responsible AI is a microlearning course that provides an introduction to responsible AI, why it’s important, and how Google implements responsible AI in their products. The course covers the following topics:
- Google’s 7 AI Principles: The course introduces Google’s 7 AI principles, which provide a framework for responsible AI applications.
- Responsible AI Practice: The course identifies the need for a responsible AI practice within an organization and recognizes that decisions made at all stages of a project have an impact on responsible AI.
- Designing AI to Fit Business Needs and Values: The course recognizes that organizations can design AI to fit their own business needs and values.
Introduction to Image Generation
Introduction to Image Generation is a microlearning course that 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. The course is designed to help learners understand the theory behind diffusion models and how to train and deploy them on Vertex AI. The course is available on Google Cloud Skills Boost and Coursera, and it is estimated to take approximately 45 minutes to complete. The course is aimed at data scientists, machine learning engineers, researchers working on developing new image generation models, and developers interested in building applications that use image generation. The course is available in English.
- Encoder-Decoder Architecture — overview of a machine learning architecture for tasks like machine translation, text summarization, and question answering. Python and Tensorflow knowledge is suggested as a prerequisite.
Attention Mechanism
In the context of artificial intelligence (AI) and machine learning, an attention mechanism is a critical component of deep learning models, particularly in the field of natural language processing (NLP) and computer vision. The primary purpose of an attention mechanism is to allow a model to focus on specific parts of the input data while making predictions or decisions. It’s inspired by the way human attention works — when we process information, we tend to pay more attention to certain elements based on their relevance to the task at hand.
The model receives input data, which could be a sequence of words in a sentence, an image, or other forms of data.
The attention mechanism calculates a “score” for each element in the input data. These scores indicate the importance or relevance of each element to the current step of the model’s prediction or decision-making process. Elements with higher scores are given more attention.
The scores are used to create weighted sums of the input elements. This means that elements with higher scores have a greater influence on the model’s output.
The weighted sums, often referred to as “context” or “attended values,” are then used by the model to make predictions or decisions. The model can use this context to focus on the most relevant parts of the input data for the current task.
Attention mechanisms have significantly improved the performance of deep learning models in various applications. In NLP, for example, they are used in models like Transformers to enable tasks like machine translation, text summarization, and question answering. In computer vision, attention mechanisms can help identify important regions of an image for object detection or image captioning.
Generative AI Studio
… is a Google Cloud console tool for rapidly prototyping and testing generative AI models. It is designed to help users prototype and customize generative AI models so they can use their capabilities in their applications. Generative AI Studio allows users to test sample prompts, design their own prompts, and customize foundation models to handle tasks that meet their application’s needs. The tool is available on Vertex AI, and it offers a range of features and options, including:
- Test models using prompt samples: Generative AI Studio includes a Prompt Gallery that contains a variety of sample prompts that are predesigned to help demonstrate model capabilities. The sample prompts are categorized by the task type, such as summarization, classification, and extraction.
- Design and save your own prompts: Users can create and save their own prompts in Generative AI Studio. When creating a new prompt, they can enter the prompt text, specify the model to use, configure parameter values, and test the prompt by generating a response. They can iterate on the prompt and its configurations until they get the desired results.
- Tune a foundation model: Generative AI Studio allows users to customize foundation models to handle tasks that meet their application’s needs. They can adjust the model’s parameters and settings to improve its performance.
- Convert between speech and text: Generative AI Studio includes a feature that allows users to convert between speech and text. They can use this feature to generate text from speech or speech from text.
The Generative AI Studio is a powerful tool for prototyping and testing generative AI models. It is designed to be accessible to a general audience and is available on Vertex AI. Google Cloud offers a range of resources and tools for building generative AI models, including courses, labs, and best practices.
