Contrastive Learning
Learn representations by pulling similar samples together and pushing dissimilar ones apart
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Embeddings & Vector Search
Dense and sparse embeddings, quantization techniques, and advanced retrieval methods for semantic search.
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Concepts
All Embeddings & Vector Search Concepts
Cross-Lingual Alignment
Align embeddings across languages for multilingual understanding
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Domain Adaptation
Adapt embeddings from source to target domains while preserving knowledge
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Binary Embeddings
Ultra-compact 1-bit representations for massive-scale retrieval
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Hybrid Retrieval Systems
Combining sparse and dense retrieval for optimal search performance
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BM25 Algorithm
Probabilistic ranking function for information retrieval with term frequency saturation
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ANN Algorithms Comparison
Compare all approximate nearest neighbor algorithms side-by-side: HNSW, IVF-PQ, LSH, Annoy, and ScaNN. Find the best approach for your use case.
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HNSW: Hierarchical Navigable Small World
Interactive visualization of HNSW - the graph-based algorithm that powers modern vector search with logarithmic complexity.
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Vector Index Structures
Explore the fundamental data structures powering vector databases: trees, graphs, hash tables, and hybrid approaches for efficient similarity search.
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IVF-PQ: Inverted File with Product Quantization
Learn how IVF-PQ combines clustering and compression to enable billion-scale vector search with minimal memory footprint.
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LSH: Locality Sensitive Hashing
Explore how LSH uses probabilistic hash functions to find similar vectors in sub-linear time, perfect for streaming and high-dimensional data.
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Vector Quantization Techniques
Master vector compression techniques from scalar to product quantization. Learn how to reduce memory usage by 10-100× while preserving search quality.
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Cross-Encoder vs Bi-Encoder
Understand the fundamental differences between independent and joint encoding architectures for neural retrieval systems.
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Dense Embeddings Space Explorer
Interactive visualization of high-dimensional vector spaces, word relationships, and semantic arithmetic operations.
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Matryoshka Embeddings
Learn about nested representations that enable flexible dimension reduction without retraining models.
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Multi-Vector Late Interaction
Explore ColBERT and other multi-vector retrieval models that use fine-grained token-level matching for superior search quality.
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Quantization Effects Simulator
Explore memory-accuracy trade-offs in embedding quantization from float32 to binary representations.
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Sparse vs Dense Embeddings
Compare lexical (BM25/TF-IDF) and semantic (BERT) retrieval approaches, understanding their trade-offs and hybrid strategies.
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