How do algorithms in Deep Learning define relationships in data?

In deep learning, algorithms define relationships in data primarily by applying neural networks. Neural networks are structured layers of interconnected nodes or neurons that mimic the way the human brain processes information. They are designed to learn complex patterns and representations from large amounts of data by adjusting the weights of connections between the nodes during the learning process.

As data is fed through the network, the layers gradually extract features and relationships, capturing intricate structures that may not be easily recognized through simpler models. This capability allows deep learning algorithms to excel in tasks such as image and speech recognition, where relationships in the data can be highly nonlinear and intricate.

While supervised learning techniques are indeed essential for training many deep learning models, they do not solely define the relationships in the data; rather, they provide a framework for learning from labeled data. Similarly, decision trees and rule-based systems represent distinct approaches to data modeling, but they do not leverage the depth and complexity inherent in neural networks.