Whitepaper Companion Podcast - Embeddings & Vector Stores | Kaggle

In this episode of "The Deep Dive," the hosts explore embeddings and vector stores, explaining how they function as low-dimensional numerical representations that capture the underlying meaning of data for AI. They discuss the efficiency and importance of embeddings in processing various data types, highlighting applications like retrieval and recommendation systems. The hosts also cover joint embeddings for multimodal data, methods for measuring effectiveness such as precision and recall, and practical considerations like model size and latency. They delve into Retrieval Augmented Generation (RAG), different types of embeddings (text, image, structured data, and graph embeddings), training processes, vector search techniques, and the use of vector databases. The discussion emphasizes the evolving nature of embedding models and their increasing accessibility, encouraging listeners to experiment with these tools for innovative projects.

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Whitepaper Companion Podcast - Embeddings & Vector Stores

Kaggle

Introduction to Embeddings and Their Significance

Applications of Embeddings: Retrieval and Joint Embeddings

Measuring Embedding Effectiveness and Practical Considerations

Retrieval Augmented Generation (RAG) and Embedding Model Advancements

Addressing Labeled Data Needs and Introduction to Text Embeddings

Word Embeddings: Word2Vec, GloVe, and Swivel

Document Embeddings: From Bag-of-Words to Doc2Vec

Deep Language Models and Advancements in Text Embeddings

Image Embeddings and Multimodal Applications

Structured Data and User/Item Embeddings

Graph Embeddings and Training Embedding Models

Vector Search and Approximate Nearest Neighbor (ANN) Techniques

HNSW, SCAN, and Vector Databases

Operational Considerations and Hybrid Search

Applications of Embeddings and Conclusion

Whitepaper Companion Podcast - Embeddings & Vector Stores

Kaggle

00:00Introduction to Embeddings and Their Significance

Introduction to Embeddings and Their Significance

02:57Applications of Embeddings: Retrieval and Joint Embeddings

Applications of Embeddings: Retrieval and Joint Embeddings

04:35Measuring Embedding Effectiveness and Practical Considerations

Measuring Embedding Effectiveness and Practical Considerations

07:12Retrieval Augmented Generation (RAG) and Embedding Model Advancements

Retrieval Augmented Generation (RAG) and Embedding Model Advancements

09:39Addressing Labeled Data Needs and Introduction to Text Embeddings

Addressing Labeled Data Needs and Introduction to Text Embeddings

12:21Word Embeddings: Word2Vec, GloVe, and Swivel

Word Embeddings: Word2Vec, GloVe, and Swivel

14:05Document Embeddings: From Bag-of-Words to Doc2Vec

Document Embeddings: From Bag-of-Words to Doc2Vec

15:55Deep Language Models and Advancements in Text Embeddings

Deep Language Models and Advancements in Text Embeddings

17:22Image Embeddings and Multimodal Applications

Image Embeddings and Multimodal Applications

19:13Structured Data and User/Item Embeddings

Structured Data and User/Item Embeddings

21:01Graph Embeddings and Training Embedding Models

Graph Embeddings and Training Embedding Models

22:32Vector Search and Approximate Nearest Neighbor (ANN) Techniques

Vector Search and Approximate Nearest Neighbor (ANN) Techniques

24:01HNSW, SCAN, and Vector Databases

HNSW, SCAN, and Vector Databases

25:27Operational Considerations and Hybrid Search

Operational Considerations and Hybrid Search

27:04Applications of Embeddings and Conclusion

Applications of Embeddings and Conclusion