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Where are Bayesian regularized Neural Networks better over the usual NNs?
That sounds interesting. Where are Bayesian regularized Neural Networks better over the usual NNs?
Bayesian Neural Networks are particularly useful in situations where the data is noisy, the number of training examples is small, or the model is complex. They can also perform better than the usual Neural Networks when dealing with high-dimensional data. Additionally, because they incorporate the uncertainty in the model's parameters, they can help detect overfitting and avoid making overly confident predictions.