Understanding and Applying Text Embeddings
Nikita Namjoshi and Andrew Ng
Free for a limited time
- 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 a 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 behavior 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.
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