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
course image

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
  • 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

Subjects

Data Science