AI Workshop: Working with Embeddings V: Contextualized Embeddings with BERT

Embeddings are the backbone of AI applications such as semantic search engines, recommendation systems, and intelligent chatbots.

By converting text into numerical vectors, they capture relationships, meanings, and patterns, enabling machines to process data intelligently. They can improve recommendation systems by clustering product descriptions for personalized results, cluster song lyrics by themes to create curated playlists or create a keyword-based matching system for CVs and job descriptions. In this hands-on workshop series, you will dive into the world of embeddings and discover how to create and apply them effectively.

Together, we will explore various paradigms, test and refine embeddings for semantic search, and evaluate their effectiveness in real-world scenarios. Each workshop stands alone, so you can attend the topics that interest you most. No prior knowledge of embeddings is required, but we recommend that you take our free OpenHPI course to brush up on the basics.

A basic to intermediate understanding of Python and a laptop are all you need to get started.

In this workshop, we will learn how advanced models such as BERT and Sentence Transformers generate embeddings that adapt to context, making them ideal for complex natural language processing (NLP) tasks. You will experiment with pre-trained models and apply them to real-world challenges.

Additional Details: Who this workshop series is for: Developers, data scientists, and natural language process enthusiasts with basic to intermediate Python skills.

What to bring: A laptop with Python installed. Further details will be provided before each workshop.

Optional preparation: Brush up on the basics of embeddings with this free OpenHPI course: https://open.hpi.de/courses/embeddingsfornlp-kisz2023

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