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You are missing a minus sign before your binary cross entropy loss function. The loss function you currently have becomes more negative (positive) if the predictions are worse (better), therefore if you minimize this loss function the model will change its weights in the wrong direction and start performing worse. To make the model perform better you either ...


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As you are looking for information from reputed resources, Tutorial why produces different results: gives reasoning why simple ML algorithm give better performance and more stable compared to neural network. Paper on industrial recognition tasks: for small amounts of training data, classical classifiers provided better performance to not pre-trained neural ...


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There could be many reasons for deep learning to have high variance in evaluation metric performance. Here are a couple of ideas: Initialization: Deep learning models are initialized with random parameter values. Different starting parameters could result in final parameter values, especially if there are few epochs. Traditional machine learning might not ...


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