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Vector Space Models and Embeddings in RAGs

Discover the power of Retrieval-Augmented Generation (RAG) in modern NLP applications. This course will teach you how to implement a RAG-based chatbot using Python and TensorFlow, focusing on text embeddings and retrieval techniques.

Axel Sirota - Pluralsight course - Vector Space Models and Embeddings in RAGs
by Axel Sirota

What you'll learn

In the ever-evolving field of natural language processing, integrating robust retrieval mechanisms with generation models is crucial for creating advanced AI systems. In this course, Vector Space Models and Embeddings in RAGs, you’ll learn to implement effective RAG-based chatbots. First, you’ll explore the foundational concepts of Retrieval-Augmented Generation and understand its significance in enhancing language models. Next, you’ll discover how to represent text data using various embedding techniques, analyzing their properties and limitations. Finally, you’ll learn how to implement these embeddings in a practical RAG system to retrieve relevant information efficiently. When you’re finished with this course, you’ll have the skills and knowledge of RAG needed to develop advanced AI chatbots capable of sophisticated text retrieval and response generation.

Table of contents

About the author

Axel Sirota - Pluralsight course - Vector Space Models and Embeddings in RAGs
Axel Sirota

Axel Sirota has a Masters degree in Mathematics with a deep interest in Deep Learning and Machine Learning Operations. After researching in Probability, Statistics and Machine Learning optimization, he is currently working at JAMPP as a Machine Learning Research Engineer leveraging customer data for making accurate predictions at Real Time Bidding.

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