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DeepLearning.AI

Sebastian Witalec

This course provides a comprehensive understanding of building multimodal search and RAG systems, focusing on the integration of various data types like text, images, and video into AI models. You will learn about contrastive learning for training multimodal models, visual instruction tuning, and the implementation of multi-vector recommender systems that enhance the capabilities of AI applications in industry settings.

Building Multimodal Search and RAG

Overview of multimodal AI technologies and their significance in modern AI applications.

Introduction to Multimodal AI

Detailed exploration of contrastive learning techniques used to train models capable of understanding multiple data types.

Contrastive Learning for Multimodal Models

Step-by-step guide on developing RAG systems that enhance LLMs by incorporating diverse data modalities.

Building RAG Systems

Techniques for creating effective search systems that operate across various data types.

Implementing Multimodal Search

Methods to train LLMs to comprehend multimodal data using visual instruction.

Visual Instruction Tuning for LLMs

Creation of recommender systems that analyze similarities across multiple modalities to suggest relevant items.

Multi-vector Recommender Systems

Practical applications of multimodal AI in industries, including analysis of invoices and flowcharts.

Industry Applications

Learn to build advanced AI systems that understand and process multiple data types simultaneously. This course will guide you through the creation of multimodal search and RAG systems, crucial for next-generation AI applications.

AI developers interested in learning how to build systems that process and reason over multiple data modalities.

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Building Multimodal Search and RAG

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