Transform how AI systems understand and connect different data modalities. This course empowers machine learning professionals to build cutting-edge cross-modal retrieval systems that bridge the gap between text and images. You'll master the technical implementation of approximate nearest-neighbor search algorithms and design sophisticated attention mechanisms that fuse visual and textual information. Through hands-on work with production-scale tools like FAISS and real datasets like Flickr30K, you'll develop the expertise to create intelligent systems that understand content across modalities—enabling breakthrough applications in search, recommendation, and content understanding that mirror how humans naturally process diverse information types.
Unify Modalities: Cross-Modal Retrieval
Unify Modalities: Cross-Modal Retrieval
This course is part of Vision & Audio AI Systems Specialization
Instructor: Hurix Digital
Included with
Recommended experience
What you'll learn
Cross-modal retrieval aligns vector spaces to bridge semantic gaps between text, images, and other data types.
ANN tools like FAISS enable fast similarity search across millions of embeddings with production-scale performance.
Attention mechanisms fuse visual and textual features by learning contextual relationships across multiple representations.
Multimodal systems balance accuracy, speed, and memory through careful index choice and parameter tuning.
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February 2026
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There are 2 modules in this course
Learners will build foundational understanding of cross-modal retrieval systems and implement approximate nearest-neighbor search algorithms using FAISS for production-scale similarity search across multimodal embeddings.
What's included
1 video2 readings1 assignment1 ungraded lab
Learners will design and implement sophisticated attention-based fusion algorithms that intelligently combine visual and textual embeddings, mastering the creation of multimodal neural architectures for advanced cross-modal AI applications.
What's included
2 readings3 assignments
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