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Discover Vector Databases: How They Work, Examples, Use Cases, Pros & Cons, Selection and Implementation. They have combined capabilities of traditional databases and standalone vector indexes while specializing for vector embeddings.
Under unsupervised learning, all the objects in the same group (cluster) should be more similar to each other than to those in other clusters; data points from different clusters should be as different as possible. Clustering allows you to find and organize data into groups that have been formed organically, rather…
Large Language Models (LLMs) have a data freshness problem. Even some of the most powerful models, like GPT-4, have no idea about recent events. Let's learn how to fix this.
Vector similarity search is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles — with incredible accuracy in sub-second timescales for billion+ size datasets.
One Machine Learning algorithm that relies on the concepts of proximity and similarity is K-Nearest Neighbor (KNN). KNN is a supervised learning algorithm capable of performing both classification and regression tasks.
BERTopic takes advantage of the superior language capabilities of (not yet sentient) transformer models and uses some other ML magic like UMAP and HDBSCAN to produce what is one of the most advanced techniques in language topic modeling today.
Product quantization (PQ) is a popular method for dramatically compressing high-dimensional vectors to use 97% less memory, and for making nearest-neighbor search speeds 5.5x faster in our tests.