What Are Vector Models in Machine Learning

In this video, Ada explores the power of vector models in driving modern technologies like facial recognition, reverse image search, recommendation engines of Spotify, Netflix or Amazon, and natural language processing applications such as ChatGPT. She explains how vector models transform complex data into vector embeddigs, enabling machines to understand and process information more effectively. With vector models, we can find visually similar images, receive personalized recommendations, and engage in natural conversations. Ada highlights the flexibility and efficiency of vector models compared to regular classification models and emphasizes the exciting possibilities they bring to today’s technology landscape.

Tutorial: https://sefiks.com/2023/07/26/the-rise-of-vector-models-in-machine-learning/

If you like this video, you may like these too:

exact nearest neighbor (k-nn) implementations:
– Vector Similarity Search with Postgres: https://youtu.be/f41sLxn1c0k
– Vector Similarity Search with MongoDB: https://youtu.be/dmprgum9Xu8
– Vector Similarity Search with Redis: https://youtu.be/eo-fTv4eYzo
– Vector Similarity Search with Cassandra: https://youtu.be/VQqHs6-4Ylg
– Vector Similarity Search with Neo4j: https://youtu.be/X-hB2kBFBXs

approximate nearest neighbor (a-nn) implementations:
– Fast Vector Similarity Search with Facebook Faiss: https://youtu.be/6AmEvDTKT-k
– Fast Vector Similarity Search with Spotify Annoy: https://youtu.be/Jpxm914o2xk
– Fast Vector Similarity Search with ElasticSearch: https://youtu.be/i4GvuOmzKzo
– Fast Vector Similarity Search with NMSLIB: https://youtu.be/EVBhO8rbKbg

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