How do we choose the best neural network architecture?

How It Works:

Match architecture to data: CNNs for spatial grids, RNNs/LSTMs for sequences, and Transformers for long-range dependencies-then prototype and benchmark.

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Key Benefits:

• Optimized performance: Leverage each type?s strengths.
• Resource efficiency: Smaller nets run faster.
• Future extensibility: Swap modules as needs evolve.

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Real-World Use Cases:

• Time-series: Use LSTMs for forecasting stock prices.
• Text classification: Fine-tune transformer encoders for sentiment.