Part 5/14:
Using pre-trained models like OpenAI’s Ada, Sentence Transformers, or Whisper for audio, data is transformed into vectors via machine learning algorithms. These models are trained on vast datasets, learning to encode relationships and contextual nuances into the vectors. For domain-specific accuracy, models are often fine-tuned with specialized data.
The role of vectors and similarity measures
Once data is embedded, the proximity of vectors (measured by cosine similarity, Euclidean distance, or Manhattan distance) indicates how closely related the data points are. This enables applications like finding books similar to "The Lord of the Rings" or retrieving documents related to a particular contract, by clustering similar embeddings in the vector space.