Useful Links
1. Introduction to Vector Search and Embeddings
2. Mathematical Foundations: Vectors and Vector Spaces
3. Data Representation: Embeddings
4. Similarity Metrics and Distance Functions
5. Nearest Neighbor Search Algorithms
6. ANN Indexing Algorithms and Data Structures
7. Vector Databases and Management Systems
8. Building Vector Search Systems: Implementation Guide
9. Advanced Topics and Optimization
10. Real-World Applications and Use Cases
11. Ethical Considerations and Best Practices
  1. Computer Science
  2. Data Science

Vector Search and Embeddings

1. Introduction to Vector Search and Embeddings
2. Mathematical Foundations: Vectors and Vector Spaces
3. Data Representation: Embeddings
4. Similarity Metrics and Distance Functions
5. Nearest Neighbor Search Algorithms
6. ANN Indexing Algorithms and Data Structures
7. Vector Databases and Management Systems
8. Building Vector Search Systems: Implementation Guide
9. Advanced Topics and Optimization
10. Real-World Applications and Use Cases
11. Ethical Considerations and Best Practices
  1. Nearest Neighbor Search Algorithms
    1. The Search Problem
      1. Definition of Nearest Neighbor Search
        1. k-Nearest Neighbors Problem
          1. Range Queries
            1. Use Cases in Information Retrieval
            2. Exact Nearest Neighbor Search
              1. Brute-Force Linear Search
                1. Algorithm Steps
                  1. Time and Space Complexity
                    1. Implementation Considerations
                    2. Scalability Challenges
                      1. Memory Usage Patterns
                        1. Latency in Large Datasets
                          1. Computational Bottlenecks
                        2. Approximate Nearest Neighbor Search
                          1. The Speed vs Accuracy Trade-off
                            1. Approximation Techniques
                              1. Quality Metrics
                                1. Performance Considerations
                                2. Core Strategies
                                  1. Space Partitioning
                                    1. Dimensionality Reduction
                                      1. Hashing Techniques
                                        1. Graph-based Approaches
                                        2. Evaluation Metrics
                                          1. Recall and Precision
                                            1. Query Latency
                                              1. Index Build Time
                                                1. Memory Usage

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                                            4. Similarity Metrics and Distance Functions

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                                            6. ANN Indexing Algorithms and Data Structures

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