Deep learning architectures are not random model names. DNN, CNN, RNN, and Transformer each appeared because data has different structure. Images need spatial patterns. Sequences need order. Modern AI needs scalable attention. That is the big picture. Core Idea Deep learning architectures evolve around one question: What structure does the data have? A basic DNN learns layered representations. A CNN is better for spatial data like images. An RNN is built for sequential data. A Transformer uses attention to model relationships more flexibly. So architecture choice is not just a preference. It is a response to the shape of the problem. The Key Structure A simple map looks like this: Deep Learning Architecture → DNN: general layered representation → CNN: spatial structure → RNN: sequential structure → Transformer: attention-based relationships The architecture changes because the data changes. The goal stays the same: learn useful representations from data.…