Non-linear encoding is a method of data representation where the relationship between input and output is not a straight line. This means that small changes in the input can lead to large changes in the output, allowing for more complex and nuanced information to be captured. It is often used in fields like signal processing and machine learning to improve the accuracy of models. In contrast to linear encoding, which assumes a direct proportionality, non-linear encoding can better handle real-world data that often exhibits irregular patterns. Techniques such as neural networks utilize non-linear encoding to learn from data, making them effective for tasks like image recognition and natural language processing.