What is the most common issue faced by neural networks during data processing?

The most common issue faced by neural networks during data processing relates to missing values. Neural networks require a complete dataset for effective training and analysis because missing values can disrupt the learning process. When input data contains missing entries, the network may struggle to capture relationships as it does not have complete information available to learn from. Depending on how the model is implemented, if it encounters missing values during training or prediction, it could lead to poor performance or even failure in processing the data.

While extreme or unusual values can also be problematic, they are generally handled through techniques such as normalization or outlier detection. Non-numeric inputs can be converted into a numeric format through various encoding methods, allowing neural networks to handle such data. Complex nonlinear relationships are actually a strength of neural networks, as they are designed to model intricate patterns in data that may not be captured by linear models. Thus, missing values stand out as the primary challenge during data processing for neural networks.