Approximate Nearest Neighbour From Scratch In Python
In this video, I dive deep into the Approximate Nearest Neighbor (ANN) algorithm and demonstrate how to implement it from scratch in Python. The ANN algorithm is a powerful technique for performing fast vector searches, even in datasets with billions of vectors. It is being actively used in the backbone of Spotify Annoy. This video will take you through the process of understanding the algorithm, implementing it, and exploring its practical applications.
We start with an overview of the Approximate Nearest Neighbor algorithm, explaining its importance and how it can be used to perform efficient searches. I will then guide you through the step-by-step implementation of ANN in Python, showing you how to build it from scratch. To make the concept more tangible, we'll run experiments with 2D vectors, providing a clear visualization of how ANN works. Although the demonstration uses 2D vectors, the algorithm can be easily adapted for multi-dimensional vector embeddings.
Vector embeddings can represent a wide array of data types, making ANN widely applicable in various fields. For instance, ANN can be used to search through song libraries on Spotify, index images on Google, or even identify faces in facial recognition models like VGG-Face or FaceNet. This versatility highlights the significance of mastering ANN for anyone involved in data science or machine learning.
Tutorial: https://sefiks.com/2023/12/31/a-step-...
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