If you are given a small dataset of 300 samples, would you choose a neural network with more hidden layers or a neural network with one hidden layer? Justify your explanation in terms of accuracy.

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Accepted Answer

For a small dataset of 300 samples, a neural network with one hidden layer would be more suitable for better accuracy.

A neural network with one hidden layer is simpler and less prone to overfitting on a small dataset.
With a small dataset, a complex neural network with more hidden layers may lead to overfitting and poor generalization.
A neural network with one hidden layer can capture the basic patterns in the data effectively.
Using a simpler model like a neural network with one hidden layer can prevent the model from memorizing noise in the data.
For a small dataset, a neural network with one hidden layer is easier to train and interpret compared to a more complex model.

If you are given a small dataset of 300 samples, would you choose a neural network with more hidden layers or a neural network with one hidden layer? Justify your explanation in terms of accuracy.

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