Understanding and Applying Text Embeddings

DeepLearning.AI via Coursera

Coursera based on 63 ratings

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Overview

Understanding and Applying Text Embeddings is a focused course designed to introduce learners to the concept and practical uses of text embeddings in natural language processing and machine learning. This course is ideal for data scientists, AI practitioners, and developers seeking to deepen their knowledge of how text data is transformed into numerical vectors that machines can understand. The course begins by explaining the theory behind embeddings, including vector representations, semantic similarity, and dimensionality reduction techniques. Learners will explore popular...

Syllabus

Project Overview

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, youll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. Youll combine semantic search with the text generation capabilities of an LLM to build a question-answering systems using Google Clouds Vertex AI.Youll also explore:(1) The properties of word and sentence embeddings.(2) How embeddings can be used to measure the semantic similarity between two pieces of text.(3) How to apply text embeddings for tasks such as classification, clustering, and outlier detection.(4) Modify the text generation behavior of an LLM by adjusting the parameters temperature, top-k, and top-p.(5) How to apply the open source ScaNN (Scalable Nearest Neighbors) library for efficient semantic search.(6) 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.