Binary Embeddings
Learn how binary embeddings use 1-bit quantization for ultra-compact vector representations, enabling billion-scale similarity search with 32x memory reduction.
Explore machine learning concepts related to retrieval. Clear explanations and practical insights.
Learn how binary embeddings use 1-bit quantization for ultra-compact vector representations, enabling billion-scale similarity search with 32x memory reduction.
Master the BM25 algorithm, the probabilistic ranking function powering Elasticsearch and Lucene for keyword-based document retrieval and search systems.
Understand the fundamental differences between independent and joint encoding architectures for neural retrieval systems.
Explore ColBERT and other multi-vector retrieval models that use fine-grained token-level matching for superior search quality.
Compare lexical (BM25/TF-IDF) and semantic (BERT) retrieval approaches, understanding their trade-offs and hybrid strategies.