Neural Network Architecture: Design Patterns for Modern AI

Neural network architecture design has evolved dramatically since the early days of simple feedforward networks. Today's architectures are sophisticated systems carefully designed for specific tasks, from image recognition to language understanding to game playing.

Convolutional Neural Networks (CNNs) revolutionized computer vision by introducing spatial hierarchies and local connectivity patterns. These architectural innovations enable networks to efficiently process visual information at multiple scales, from low-level edges to high-level concepts.

Transformers have become the architecture of choice for natural language processing and beyond. Their attention mechanism allows models to focus on relevant information regardless of distance in the input, enabling unprecedented performance on language tasks. Recent work has extended transformers to vision, multimodal learning, and even protein structure prediction.

Recurrent architectures like LSTMs and GRUs remain important for sequential data processing, though transformers have largely superseded them for many tasks. Graph neural networks represent another important architectural family, enabling learning on irregular structures like social networks, molecules, and knowledge graphs.

The art of architecture design involves understanding your data, your task, and your computational constraints. While pre-trained models and transfer learning have reduced the need for training from scratch, choosing and adapting the right architecture remains crucial for achieving optimal performance.