Understanding and Applying Text Embeddings
Instructor: Nikita Namjoshi and Andrew Ng
- Beginner
- 1 Hour 16 Minutes
- 8 Video Lessons
- 6 Code Examples
- Instructor: Nikita Namjoshi and Andrew Ng
What you'll learn
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
About 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.
Course Outline
8 Lessons・6 Code ExamplesIntroduction
Video・2 mins
Getting Started With Text Embeddings
Video with code examples・12 mins
Understanding Text Embeddings
Video・9 mins
Visualizing Embeddings
Video with code examples・16 mins
Applications of Embeddings
Video with code examples・15 mins
Text Generation with Vertex AI
Video with code examples・19 mins
Building a Q&A System Using Semantic Search
Video with code examples・1 min
Optional - Google Cloud Setup
Code examples・1 min
Conclusion
Video・1 min
Instructors
Nikita Namjoshi
Developer Advocate at Google Cloud
Understanding and Applying Text Embeddings
- Beginner
- 1 Hour 16 Minutes
- 8 Video Lessons
- 6 Code Examples
- Instructor: Nikita Namjoshi and Andrew Ng
Course access is free for a limited time during the DeepLearning.AI learning platform beta!
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