Lowering the Entry Threshold for Neural Vector Search - Applying Similarity Learning

Overview

Explore similarity learning for efficient neural search implementation, reducing data requirements and training time while addressing domain-specific challenges.

Syllabus

• Introduction to Neural Vector Search
Overview of Vector Search and Embeddings
Importance in Modern AI Applications
Key Challenges and Objectives
• Fundamentals of Similarity Learning
Definition and Scope
Types of Similarity Measures
Applications in Neural Search
• Data Requirements in Neural Search
Understanding Data Complexity
Strategies to Minimize Data Needs
Feature Selection and Dimensionality Reduction
• Techniques for Efficient Neural Search Implementation
Neural Network Architectures for Vector Similarity
Approximate Nearest Neighbors (ANN) Algorithms
Indexing Strategies for Fast Retrieval
• Reducing Training Time in Similarity Learning
Transfer Learning and Pre-trained Models
Incremental and Online Learning Approaches
Hardware and Software Optimization Techniques
• Addressing Domain-Specific Challenges
Customizing Models for Specific Domains
Handling Sparse and Imbalanced Data
Domain Adaptation and Generalization
• Evaluating and Benchmarking Neural Vector Search
Performance Metrics and Evaluation Protocols
Benchmark Datasets and Challenges
Case Studies of Successful Implementations
• Emerging Trends and Future Directions
Advances in Similarity Learning Techniques
Integration with Other AI Technologies
Prospects for Low-Resource Environments
• Conclusion and Course Wrap-Up
Summary of Key Learnings
Resources for Continued Learning
Final Q&A Session

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Subjects

Data Science