Structured data refers to data that is organized into a well-defined format, typically in rows and columns, such as data in spreadsheets or relational databases. It is easy to process and analyze because it is highly organized, with clear relationships between different data points. Neural networks trained on structured data often use techniques like decision trees or support vector machines, but can also handle structured inputs effectively.
Unstructured data, on the other hand, lacks a predefined format and includes data types like text, images, video, and audio. This type of data is much more complex and requires specialized neural network models like convolutional neural networks (CNNs) for images or recurrent neural networks (RNNs) for sequences.
The key difference is that structured data is easier to handle and often requires less preprocessing, while unstructured data demands more sophisticated models and techniques for extraction of meaningful patterns.