What is model overparameterization in Keras and why is it risky?

Overparameterization occurs when a model has more parameters than necessary, increasing overfitting risk.

In Keras, large Dense or Conv layers can lead to excessive weights, making the model memorize training data instead of generalizing. This increases computational cost and reduces generalization performance. Techniques like pruning, dropout, and weight regularization mitigate this.