In other words, why have improvements to neural networks led to an increase in hyperparameters? Are hyperparameters related to some fundamental flaw of neural networks?
A nice question by Yuefei Zhang . Generally when we have improve to neural network it led to an increase in hyperparameters due to availability of multiple layers for designing model architecture in deep learning, due to using of multiple optimization algorithms, due to regularizing the models etc.
Secondly hyperparameters are not necessarily related to a fundamental flaw of neural networks; rather, they are inherent to the nature of the models and the challenges they address. Neural networks, including deep learning models, are highly flexible and adaptable, capable of learning complex patterns and representations from data.
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Jogeswar Tripathy
Hyperparameters control aspects like network architecture, learning rates, and regularization, which are crucial for optimizing performance. They don't indicate a fundamental flaw but reflect the sophistication and customization required to tackle diverse tasks effectively. As networks evolve to handle more complex data and tasks, hyperparameter tuning becomes essential for achieving optimal performance.