Intermediate · Neural Networks
Embedding layer
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A hidden neural network layer that learns lower-dimensional representations (embeddings) for high-dimensional categorical data.
Technical Definition
A hidden neural network layer that learns lower-dimensional representations (embeddings) for high-dimensional categorical data.
How it works
An embedding layer is a specialized neural network layer designed to handle high-dimensional categorical features. Instead of using the raw, sparse representation, it learns a dense, lower-dimensional vector representation for each category. This makes the model more efficient and effective by capturing semantic relationships between categories.
Related Concepts
- Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
- Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
- Dimensionality reduction — The process of reducing the number of features or variables in a dataset while retaining essential information.