Learn how multimodality works by implementing contrastive learning, and see how it can be used to build modality-independent embeddings for seamless any-to-any retrieval.
Short CourseIntermediate1 hour 22 mins
Building Multimodal Search and RAG
Instructor: Sebastian Witalec
- Intermediate
- 1 hour 22 mins
- 8 Video Lessons
- 6 Code Examples
- Instructor: Sebastian Witalec
What you'll learn
Build multimodal RAG systems that retrieve multimodal context and reason over it to generate more relevant answers.
Implement industry applications of multimodal search and build multi-vector recommender systems.
About this course
Learn how to build multimodal search and RAG systems. RAG systems enhance an LLM by incorporating proprietary data into the prompt context. Typically, RAG applications use text documents, but, what if the desired context includes multimedia like images, audio, and video? This course covers the technical aspects of implementing RAG with multimodal data to accomplish this.
- Learn how multimodal models are trained through contrastive learning and implement it on a real dataset.
- Build any-to-any multimodal search to retrieve relevant context across different data types.
- Learn how LLMs are trained to understand multimodal data through visual instruction tuning and use them on multiple image reasoning examples
- Implement an end-to-end multimodal RAG system that analyzes retrieved multimodal context to generate insightful answers
- Explore industry applications like visually analyzing invoices and flowcharts to output structured data.
- Create a multi-vector recommender system that suggests relevant items by comparing their similarities across multiple modalities.
As AI systems increasingly need to process and reason over multiple data modalities, learning how to build such systems is an important skill for AI developers.
This course equips you with the key skills to embed, retrieve, and generate across different modalities. By gaining a strong foundation in multimodal AI, you’ll be prepared to build smarter search, RAG, and recommender systems.
Who should join?
This course is for anyone who wants to start building their own multimodal applications. Basic Python knowledge, as well as familiarity with RAG is recommended to get the most out of this course.
Course Outline
8 Lessons・6 Code ExamplesIntroduction
Video・3 minsOverview of Multimodality
Video with Code Example・23 minsMultimodal Search
Video with Code Example・15 minsLarge Multimodal Models (LMMs)
Video with Code Example・9 minsMultimodal RAG (MM-RAG)
Video with Code Example・9 minsIndustry Applications
Video with Code Example・7 minsMultimodal Recommender System
Video with Code Example・14 minsConclusion
Video・1 min
Instructor
Course access is free for a limited time during the DeepLearning.AI learning platform beta!
Want to learn more about Generative AI?
Keep learning with updates on curated AI news, courses, and events, as well as Andrew’s thoughts from DeepLearning.AI!