Short Course on Understanding & Applying Text Embeddings with Vertex AI by DeepLearning AI & Google Cloud. Free for a limited time only!
About DeepLearning AI
DeepLearning AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community. DeepLearning AI has created high-quality AI programs on Coursera that have gained an extensive global following. By providing a platform for education and fostering a tight-knit community, DeepLearning AI has become the pathway for anyone looking to build an AI career.
Google Cloud
Whether your business is early in its journey or well on its way to digital transformation, Google Cloud can help solve your toughest challenges. There has never been a more exciting time in technology. They are experiencing one of the most significant shifts in history, where AI is creating entirely new ways to solve problems, engage customers, and work more efficiently. Google Cloud is ready to help organizations build a new way forward in an increasingly AI-driven world.
Course Highlights
- Use text embeddings to capture the meaning of sentences and paragraphs.
- Apply text embeddings for tasks like text clustering, classification, and outlier detection.
- Use Google Cloud’s Vertex AI to build a question-answering system.
What you’ll learn in this course?
The Vertex AI Text-Embeddings API enhances the process of generating text embeddings. These text embeddings, which are numerical representations of text, play a pivotal role in many tasks involving the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions.
During this course, you’ll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You’ll combine semantic search with the text generation capabilities of an LLM to build question-answering systems using Google Cloud’s Vertex AI.
You’ll also explore:
- The properties of word and sentence embeddings.
- How embeddings can be used to measure the semantic similarity between two pieces of text.
- How to apply text embeddings for tasks such as classification, clustering, and outlier detection
- Modify the text generation behaviour of an LLM by adjusting the parameters temperature, top-k, and top-p.
- How to apply the open source ScaNN (Scalable Nearest Neighbors) library for efficient semantic search.
- How to build a Q&A system by combining semantic search with an LLM.
- Upon successful completion of this course, you will grasp the underlying concepts of using text embeddings, and will also gain proficiency in generating embeddings and integrating them into common LLM applications.
Who should join?
Anyone with basic Python knowledge who wants to learn about text embeddings and how to apply them to common NLP tasks.
How to Register?
Interested candidates can directly register through this link.
Fee
Free for a limited time only!
Instructors
- Nikita Namjoshi, Developer Advocate for Generative AI at Google Cloud
- Andrew Ng, Founder, DeepLearning.AI; Co-founder, Coursera
