Building a RAG Solution from Scratch
Building a RAG Solution from Scratch
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 12m | 840 MB
Instructor: Axel Sirota
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 12m | 840 MB
Instructor: Axel Sirota
In this course, Axel Sirota introduces Retrieval-Augmented Generation (RAG) as a powerful technique for enhancing the capabilities of Large Language Models (LLMs). Learn the foundational concepts and practical applications of RAG, focusing on creating chatbots and decision support systems across various domains. Using the MIMIC-III dataset to create a healthcare chatbot that can answer questions or suggest a diagnosis as an example, get hands-on experience in building RAG systems with TensorFlow, Keras, and HuggingFace. By the end of the course, you will be equipped to deploy RAG solutions that integrate robust retrieval mechanisms with generative models, applicable in fields like healthcare, legal, and customer service.
Learning objectives
- Understand the principles and architecture of RAG systems.
- Implement RAG solutions using TensorFlow, Keras, and HuggingFace.
- Develop and deploy chatbots and decision support tools.
- Explore various applications of RAG across different industries.
- Analyze research developments and future trends in RAG.
Building a RAG Solution from Scratch
