Explore Machine Learning principles and practices which make modern semantic similarity search possible. Apply Qdrant and vector search capabilities to your ML projects.
A comprehensive guide to modern sparse neural retrievers: COIL, TILDEv2, SPLADE, and more. Find out how they work and learn how to use them effectively.
Evgeniya Sukhodolskaya
·October 23, 2024
A summary of my work and experience at Qdrant Summer of Code 2024.
Huong (Celine) Hoang
·October 14, 2024
We recently discovered that embedding models can become late interaction models & can perform surprisingly well in some scenarios. See what we learned here.
Kacper Łukawski
·August 14, 2024
Introducing BM42 - a new sparse embedding approach, which combines the benefits of exact keyword search with the intelligence of transformers.
Andrey Vasnetsov
·July 01, 2024
Learn when and how to use layer recycling to achieve different performance targets.
Yusuf Sarıgöz
·August 23, 2022
Learn how to train a similarity model that can retrieve similar car images in novel categories.
Yusuf Sarıgöz
·June 28, 2022
Practical use of metric learning for anomaly detection. A way to match the results of a classification-based approach with only ~0.6% of the labeled data.
Yusuf Sarıgöz
·May 04, 2022
What are the advantages of Triplet Loss over Contrastive loss and how to efficiently implement it?
Yusuf Sarıgöz
·March 24, 2022
Practical recommendations on how to train a matching model and serve it in production. Even with no labeled data.
Andrei Vasnetsov
·May 15, 2021