Embeddings

Vector representations of data in multidimensional space

Overview

Embeddings are numerical vector representations that transform data into meaningful points in a high-dimensional space. Think of them as coordinates that capture the essence and relationships between objects - whether they’re words, images, or any other type of data. These vectors serve as a sophisticated translation layer, converting complex information into a mathematical format that machine learning models can process and understand.

Key Concepts

• Dimensional Meaning: Each dimension in the embedding space represents different features or aspects of the data
• Similarity Metrics: The closer two vectors are in the embedding space, the more semantically similar their corresponding items
• Learned Representations: Embeddings are typically learned from data, allowing them to capture nuanced relationships

Types of Embeddings

1. Word Embeddings
   • Transform individual words into vectors (e.g., “cat” → [0.2, -0.5, 0.1])
   • Popular models: Word2Vec, GloVe, FastText
   • Capture semantic relationships like: king - man + woman ≈ queen
2. Contextual Embeddings
   • Generate dynamic vectors based on context
   • Same word can have different embeddings in different contexts
   • Examples: BERT, GPT, RoBERTa
3. Sentence/Document Embeddings
   • Represent entire text segments as single vectors
   • Preserve semantic meaning across longer contexts
   • Used for document similarity, clustering, and retrieval

Learning Resources

• 📄 Medium Article: Word Embeddings Deep Dive
  • Comprehensive introduction to word vector representations
• 📄 Medium Article: Contextual Embedding Guide
  • Advanced concepts in context-aware embeddings
• 📄 Medium Article: Sentence Embedding Techniques
  • Modern approaches to sentence-level embeddings
• 🟠 Colab Notebook: Interactive Word2Vec Tutorial
  • Hands-on implementation with detailed explanations

Additional Resources