In my learning system, compression is implemented through various steps to ensure efficient data processing and extraction of essential information. Here is a step-by-step explanation of how compression is integrated into my learning system:

The process begins with the input of raw data, which can be in the form of text, images, or other types of information. This raw data contains a wealth of details and complexities that need to be distilled into essential patterns and features for learning.

The next step involves feature extraction, where the raw data is analyzed to identify key features that are relevant for learning. This process helps in reducing the dimensionality of the data and focusing on the most important aspects.