Dense Embeddings
How dense embeddings turn meaning into geometry: word2vec, GloVe, and contextual models, vector arithmetic, cosine similarity, and where the field is heading.
Dense and sparse embeddings, quantization, and vector search for semantic retrieval.
How dense embeddings turn meaning into geometry: word2vec, GloVe, and contextual models, vector arithmetic, cosine similarity, and where the field is heading.
How sparse retrieval (BM25/TF-IDF), dense retrieval (BERT-style embeddings), and hybrid systems that combine both compare on recall, semantic understanding, computational cost, and operational complexity for modern search.
Master the BM25 algorithm, the probabilistic ranking function powering Elasticsearch and Lucene for keyword-based document retrieval and search systems.
How a transformer’s per-token outputs become one embedding: CLS, mean, max, last-token, and attention pooling — what each does and when to use it.
Master contrastive learning for vector embeddings: how InfoNCE loss and self-supervised techniques train models to create high-quality semantic representations.
Matryoshka embeddings: nested representations enabling dimension reduction by simple truncation without model retraining for flexible retrieval.
Domain adaptation for embeddings: transfer learning to fine-tune retrieval models across domains while preventing catastrophic forgetting.
Learn cross-lingual embedding alignment techniques like VecMap and MUSE for multilingual vector retrieval and zero-shot language transfer in 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.
Build hybrid retrieval systems combining BM25 sparse search with dense vector embeddings using reciprocal rank fusion for superior semantic search performance.
Embedding quantization simulator: explore memory-accuracy trade-offs from float32 to int8 and binary representations for retrieval.
Master vector compression techniques from scalar to product quantization. Learn how to reduce memory usage by 10-100× while preserving search quality.
Learn how binary embeddings use 1-bit quantization for ultra-compact vector representations, enabling billion-scale similarity search with 32x memory reduction.
Explore the fundamental data structures powering vector databases: trees, graphs, hash tables, and hybrid approaches for efficient similarity search.
How HNSW, IVF-PQ, and LSH compare for approximate nearest neighbor (ANN) search — recall, latency, memory, build cost, and update characteristics — with Annoy, ScaNN, and DiskANN included for completeness.
Explore how LSH uses probabilistic hash functions to find similar vectors in sub-linear time, perfect for streaming and high-dimensional data.
Learn how IVF-PQ combines clustering and compression to enable billion-scale vector search with minimal memory footprint.
How HNSW navigates a layered proximity graph to find nearest neighbors in logarithmic time — the default in-memory index of modern vector databases.