Can you explain the process and significance of optimizing deep learning models for better performance and efficiency?

Optimizing deep learning models involves techniques such as hyperparameter tuning, model pruning, quantization, and using efficient architectures like CNNs or RNNs. This process enhances model accuracy, reduces computational costs, and speeds up inference, making the models more practical for real-world applications.

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