What You Need to Know Before
You Start
Starts 2 June 2025 14:49
Ends 2 June 2025
00
days
00
hours
00
minutes
00
seconds
Lowering the Entry Threshold for Neural Vector Search - Applying Similarity Learning
Explore similarity learning for efficient neural search implementation, reducing data requirements and training time while addressing domain-specific challenges.
OpenSource Connections
via YouTube
OpenSource Connections
2408 Courses
33 minutes
Optional upgrade avallable
Not Specified
Progress at your own speed
Free Video
Optional upgrade avallable
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
- Fundamentals of Similarity Learning
- Data Requirements in Neural Search
- Techniques for Efficient Neural Search Implementation
- Reducing Training Time in Similarity Learning
- Addressing Domain-Specific Challenges
- Evaluating and Benchmarking Neural Vector Search
- Emerging Trends and Future Directions
- Conclusion and Course Wrap-Up
Overview of Vector Search and Embeddings
Importance in Modern AI Applications
Key Challenges and Objectives
Definition and Scope
Types of Similarity Measures
Applications in Neural Search
Understanding Data Complexity
Strategies to Minimize Data Needs
Feature Selection and Dimensionality Reduction
Neural Network Architectures for Vector Similarity
Approximate Nearest Neighbors (ANN) Algorithms
Indexing Strategies for Fast Retrieval
Transfer Learning and Pre-trained Models
Incremental and Online Learning Approaches
Hardware and Software Optimization Techniques
Customizing Models for Specific Domains
Handling Sparse and Imbalanced Data
Domain Adaptation and Generalization
Performance Metrics and Evaluation Protocols
Benchmark Datasets and Challenges
Case Studies of Successful Implementations
Advances in Similarity Learning Techniques
Integration with Other AI Technologies
Prospects for Low-Resource Environments
Summary of Key Learnings
Resources for Continued Learning
Final Q&A Session
Subjects
Data Science