Advanced · Neural Networks

Representation Learning

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The process of automatically discovering meaningful representations of data from raw inputs.

Technical Definition

The process of automatically discovering meaningful representations of data from raw inputs.

How it works

Representation learning, or feature learning, aims to transform raw data into a more abstract and useful form that facilitates the learning of a specific task. Instead of manual feature engineering, models automatically learn relevant features, reducing the need for human intervention. Deep learning architectures like autoencoders and deep neural networks are powerful tools for representation learning, creating embeddings that capture intrinsic data characteristics.

Related Concepts

• Embedding — A dense vector representation that captures semantic meaning, mapping discrete items like words into continuous mathematical space.
• Autoencoder — A neural network that learns compressed representations by training to reconstruct its own input through a bottleneck layer.