In our data driven world, embeddings getting more and more important. Embeddings are a method for representing complex data in dense vector spaces, tracing their evolution from pre-transformer approaches to the powerful transformer-based models used today. The pivotal role of embeddings across diverse applications are reaching from natural language processing to computer vision, where they facilitate breakthroughs in data understanding and processing. Additionally, it’s crucial to know the strategies for evaluating and managing embeddings, ensuring their effective application across various domains.

In this session, you will learn more about:

• The differences between pre-transformer embeddings and post-transformer embeddings
• What are the real-world use cases of embeddings in various domains, including natural language processing, computer vision, and recommendation systems
• How to manage and evaluate the quality of embeddings and assessing their performance for different tasks