24 Courses on Artificial Intelligence by Google
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Introduction to Image Generation
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.
Machine Learning Operations (MLOps) for Generative AI
This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
Introduction to Vertex AI Studio
This course introduces Vertex AI Studio, a tool for prototyping and customizing generative AI models. Through immersive lessons, engaging demos, and a hands-on lab, you'll explore the generative AI workflow and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, and model tuning.
Responsible AI: Applying AI Principles with Google Cloud
As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly.
Encoder-Decoder Architecture
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.
Create Image Captioning Models
This course teaches you how to create an image captioning model by using deep learning.
Transformer Models and BERT Model
This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.
Introduction to Responsible AI
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.
Introduction to Large Language Models
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.
Recommendation Systems
We've designed this course to expand your knowledge of recommendation systems and explain different models used in recommendation, including matrix factorization and deep neural networks.