Understanding Vector Embeddings
Eiman explains the concept of deterministic embeddings and their role in transforming data for models, emphasizing that there is no one-to-one mapping with original tokens. Jon highlights the importance of tokenization in large language models, illustrating how words are broken down into subword tokens and represented in high-dimensional embedding spaces, akin to coordinates on a map.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
SDS 843: Safe, Fast and Efficient AI — with Protopia’s Dr. Eiman Ebrahimi
Related Questions
How do vector embeddings work in the context of the episode Mindscape 280 | François Chollet on Deep Learning and the Meaning of Intelligence and the clip Understanding Language Models?
How do vector embeddings work in the context of the episode Mindscape 280 | François Chollet on Deep Learning and the Meaning of Intelligence and the clip Understanding Language Models?
How do vector embeddings work?