The goal of this basic-level microlearning course is to define generative artificial intelligence (AI), explain its uses, and show how it differs from more conventional machine learning techniques.
This course covers large language models (LLMs), their use cases, and how to use prompt optimization to improve LLM performance. It is an entry-level micro-learning course. It also discusses the Google tools that you can use to create your own Gen AI applications.
This microlearning course is designed for beginners and aims to explain responsible AI, what it is, why it matters, and how Google uses it in its products. The seven AI tenets developed by Google are also introduced.
After finishing the Introduction to Large Language Models, Introduction to Generative AI, and Introduction to Responsible AI courses, you can obtain a skill badge. You will be able to prove that you understand the fundamentals of generative AI by passing the final quiz.
Many of the most advanced image generation models and tools available on Google Cloud are based on diffusion models. You will learn about the theory underlying diffusion models in this course, along with how to train and use them on Vertex AI.
A brief overview of the encoder-decoder architecture is provided in this course. It is a popular and strong machine learning architecture used for sequence-to-sequence tasks like text summarization, question answering, and machine translation. You gain knowledge about how to train and serve these models as well as the essential elements of the encoder-decoder architecture.
You will learn about the attention mechanism in this course. It is an effective tool that allows neural networks to concentrate on particular segments of an input sequence.
In this course, you will learn about the Bidirectional Encoder Representations from Transformers (BERT) model and the Transformer architecture. You gain knowledge of the self-attention mechanism and other key elements of the Transformer architecture, as well as how the BERT model is constructed using them.
You will learn how to use deep learning to create an image captioning model in this course. You gain knowledge about the various parts of an image captioning model, including the encoder and decoder, as well as how to train and assess your model.
This course introduces you to Vertex AI’s Generative AI Studio, a product that facilitates the prototyping and customization of generative AI models for use in applications.
You will learn the skills, methods, and best practices for creating efficient prompts in this course. Prompt engineering fundamentals are covered in this course, which then moves on to more complex prompt strategies.
You will discover how to use a large language model (LLM) to quickly create new and potent applications in ChatGPT Prompt Engineering for Developers.
AI is changing our way of working, playing, and living. The need for AI and machine learning specialists is expanding quickly, either by enabling new technologies like recommendation systems and self-driving cars or by enhancing more established ones like search engines and medical diagnostics. You will be able to future-proof your career and solve significant real-world problems by taking this course as a first step.
The fundamentals of generative AI are covered in this course by generative AI specialist Pinar Seyhan Demirdag. Subjects covered include what generative AI is, how it operates, how to create your content, various model types, future predictions, and ethical implications.