Graph-Based Approximate Nearest Neighbors (ANN) and HNSW

In the last decade graph-based indexes have gained massive popularity due to their effectiveness, generality and dynamic nature and are now the backbone of many practical large-scale approximate nearest neighbor search solutions. In this practical workshop led by Yury Malkov, author of HNSW, and James Briggs, Staff Developer Advocate at Pinecone, you’ll get an overview of the graph algorithms for nearest neighbor search, and, in particular, HNSW, so you can apply them to your applications. Learn more about HNSW: https://www.pinecone.io/learn/hnsw/ Try graph-based indexes on Pinecone: https://app.pinecone.io Learn more about Pinecone: https://www.pinecone.io