Courses
Fine-Tune Embedding Models for Semantic Search
Master the art of teaching machines to understand human language with our free course! Dive into vector representations and embeddings, explore sentence and vision transformers, and learn to fine-tune embedding models for different applications. Plus much more!
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In this course, you will learn how Natural Language Processing (NLP) powers semantic search and its real-world applications. Such applications include search engines, virtual assistants and recommendation systems as implemented by leading technology companies like Google, Amazon and Netflix. From vector search fundamentals to fine-tuning embedding models, you will gain a comprehensive understanding of modern NLP and semantic search techniques, regardless of your background.
In addition to mastering the basics of vector search, this course will also show you how to fine-tune embedding models. Learning to fine-tune these models allows you to customize them for different uses, making them more effective in real-life situations. By the end of the course, you'll not only understand the theory but also gain practical experience applying these techniques to solve everyday NLP problems!
Introduction to Vector Embeddings
Learn about vector embeddings and their role in data retrieval.
Foundations of Embedding Models
Explore the fundamentals of generating vector embeddings.
Introduction to Vector Databases
Learn the key concepts behind vector databases and vector search.
Introduction to Sentence Transformers
Dive into how semantic transformers and how they actually work.
Training and Fine-Tuning Models
Learn How To Data-your own sentence transformers.
Introduction to Vision Transformers
Discover how computers understand and interpret images.
Training and Fine-Tuning Vision Transformers
Learn how to fine-tune pre-trained vision transformers by fine-tuning them for image classification.
Fine-Tuning CLIP Models
Enhance your CLIP model by fine-tuning them for image-to-text classification.
New modules coming soon!
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