Embeddings
1. Overview
1.1. Definition:
- Embeddings are a method to represent categorical data, usually words or items, into continuous vector spaces. They aim to capture the semantic meaning of these items in a form that is conducive to machine learning models.
1.2. Mechanisms:
- Neural Networks: Most embeddings are learned as part of a neural network-based model.
- Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) or t-SNE (t-Distributed Stochastic Neighbor Embedding) can be used to create embeddings.
1.3. Benefits:
- Semantic Meaning: Embeddings allow capturing relationships and meanings between items (e.g., words with similar meanings have similar embeddings).
- Dimensionality Reduction: Transforming data into lower dimensions helps in reducing computational cost while preserving essential information.
1.4. Challenges:
- Interpretability: Embeddings are often dense vectors that aren't directly interpretable by humans.
- Training Data Bias: Embeddings can inherit biases present in the training datasets.
- Domain-Specificity: Embeddings trained on one domain might not generalize well to another without fine-tuning.
1.5. Trends:
- Use in multimodal AI scenarios combining text, image, and sound data for richer representations.
Tags::ml: