Techniques & Methods
Feature Extraction
Feature extraction transforms raw input data into a set of informative, discriminative features that machine learning models can work with effectively. Traditional ML required careful manual feature engineering; deep learning learns features automatically from raw data through successive layers.
In the context of transfer learning, using a pre-trained model as a feature extractor (freezing its weights and using its representations as inputs to a downstream model) is a lightweight alternative to full fine-tuning.
Authority Links
Related Terms
Core Concepts
Deep Learning
Subset of ML using neural networks with many layers to analyze complex data representations.
Techniques & Methods
Transfer Learning
Leveraging knowledge learned from one task or domain to improve performance on a related one.
Model Components
Embeddings
Dense numerical vectors that represent text, images, or other content in a high-dimensional space where semantically similar items are geometrically close — the foundational data structure for semantic search and RAG retrieval.
Techniques & Methods
Pre-training
Initial phase where a model learns general representations from large datasets before task-specific fine-tuning.