How Deep Learning Architectures Evolved — From DNNs to Transformers

Short summary

Deep learning architectures are designed around data structure: DNNs for general features, CNNs for spatial data like images, RNNs for sequential data, and Transformers for tasks needing long-range relationships via attention. Key evolution milestones include LeNet and AlexNet proving CNNs for computer vision, while Transformers replaced RNNs as the foundation for language and multimodal AI systems. Selecting architecture should start with data type first—match structure to problem, then tune implementation details.

• •Architecture selection follows data structure (spatial, sequential, relational)
• •CNN for images, RNN for time series, Transformer for language and multimodal
• •Evolution timeline: LeNet→AlexNet→ResNet (vision), RNN→Attention→Transformer (sequences)

Generated with AI, which can make mistakes.