What is unsupervised learning, and how is it implemented in PyBrain?
Unsupervised learning is a type of machine learning where the model learns patterns and structure from input data without explicit supervision or labeled output. Unlike supervised learning, there are no predefined output labels in unsupervised learning. Instead, the model aims to discover hidden patterns, relationships, or representations within the data.
In PyBrain, unsupervised learning is implemented using various algorithms designed for tasks such as clustering, dimensionality reduction, and feature learning.
