Short CourseBeginner1 Hour 16 Minutes

Understanding and Applying Text Embeddings

Instructors: Nikita Namjoshi and Andrew Ng

Google Cloud
  • Beginner
  • 1 Hour 16 Minutes
  • 8 Video Lessons
  • 6 Code Examples

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 Examples
  • Introduction

    Video2 mins

  • Getting Started With Text Embeddings

    Video with code examples12 mins

  • Understanding Text Embeddings

    Video9 mins

  • Visualizing Embeddings

    Video with code examples16 mins

  • Applications of Embeddings

    Video with code examples15 mins

  • Text Generation with Vertex AI

    Video with code examples19 mins

  • Building a Q&A System Using Semantic Search

    Video with code examples1 min

  • Optional - Google Cloud Setup

    Code examples1 min

  • Conclusion

    Video1 min

Instructors

Nikita Namjoshi

Nikita Namjoshi

Developer Advocate at Google Cloud

Andrew Ng

Andrew Ng

Founder, DeepLearning.AI; Co-founder, Coursera

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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