Vector Search and Embeddings
Geometric Interpretation of Similarity
Relationship Between Distance and Similarity
Choosing Appropriate Metrics
Mathematical Definition
Geometric Interpretation
Normalization Properties
Use Cases in Text and Image Search
L2 Norm Calculation
Properties and Characteristics
Sensitivity to Dimensionality
Applications and Limitations
L1 Norm Calculation
Robustness to Outliers
Use Cases and Applications
Relationship to Cosine Similarity
Magnitude Sensitivity
Computational Efficiency
Hamming Distance
Jaccard Similarity
Minkowski Distance
Mahalanobis Distance
Data Characteristics
Application Requirements
Computational Constraints
Interpretability Needs
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3. Data Representation: Embeddings
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5. Nearest Neighbor Search Algorithms