Describe how to build and optimize deep learning models for structured data.

Deep learning for structured data requires careful architecture design and optimization techniques for tabular datasets.\n\n• Architecture: Wide & Deep networks, TabNet, Neural Oblivious Decision Trees\n• Preprocessing: Feature scaling, embedding layers for categorical variables\n• Optimization: Learning rate scheduling, batch normalization, dropout\n• Comparison: Benchmark against traditional ML methods (XGBoost, Random Forest)\n\nExample: Customer lifetime value prediction uses TabNet architecture with categorical embeddings, sequential attention mechanism, and feature selection. Compares performance against XGBoost baseline and optimizes hyperparameters using Bayesian optimization.