Approximate Nearest Neighbours in FAISS: Cell Probe 101

In this video, we will learn about the capabilities of Facebook's FAISS library in the context of vector search. We will discuss the technical framework of Approximate Nearest Neighbours and its implementation using Cell Probe methods. We will illustrate this with a visualization of 10,000 2D arrays and detail how the vector space is partitioned.
Additionally, we'll explain the role of the K-means algorithm in FAISS's partitioning process, the steps to train your index, and methods to identify centroids that denote the cells.
#FAISS #VectorSearch #NearestNeighbours #CellProbeMethods #DataVisualization #MachineLearning #FacebookAI #KmeansAlgorithm #BruteForceSearch #AIExplained
Blog: www.markhneedham.com/blog/202...
Notebook: github.com/mneedham/LearnData...

[@diptimanraichaudhuri6477]

Mark - top notch post ! I have been following your duckdb videos, and the quality of your videos just keeps improving ! !

[@learndatawithmark]

Thanks, I'm glad you like them!

[@eugenekrasikov2414]

thank you, great content!

[@philtoa334]

Nice.

[@AnudeepKolluri]

Hi, what is IVF_FLAT? it sounds same to the concept u explained in the video

[@learndatawithmark]

I find that in the docs for Milvus (a vector db) - milvus.io/docs/index.md - and you're right, it does sound similar if not the same.

[@user-fs9bu5ve3m]

Please, don't fast-forward the coding part. I have to watch you video with 2.0x slow motion. Then only I can understand.

[@learndatawithmark]

Thanks for the advice - lemme see how I can do that in the future videos

[@user-fs9bu5ve3m]

@@learndatawithmark Thanks for considering