Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Faiss - Vector Compression with PQ and IVFPQ in Python

James Briggs via YouTube

Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off
One annual plan covers every course and certificate on Coursera. 40% off for a limited time.
Get Full Access
Learn how to implement vector compression using Product Quantization (PQ) and Inverted File Product Quantization (IVFPQ) with Faiss in Python. Explore the process of building a PQ index, combining PQ with an Inverted File step to enhance search speed, and work with the Sift1M dataset. Gain insights into memory usage optimization and composite indexing techniques for efficient semantic search. Follow along with practical demonstrations and code examples to understand the advantages of using libraries like Faiss for production-ready vector similarity search implementations.

Syllabus

Intro
Demonstration
Dataset
Initialization
Adding vectors
Memory Usage
Composite Index
Results

Taught by

James Briggs

Reviews

Start your review of Faiss - Vector Compression with PQ and IVFPQ in Python

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.