Linear regression is often considered the simplest neural network, but as networks grow deeper with added non-linear transformations, they enable complex feature engineering. How do these advancements in neural networks, especially with convolutional neural networks for image classification, expand the possibilities for data representation and problem-solving?

Can you share any experiences or insights on leveraging deep learning models for unconventional data types?