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networks\\nJonasTeuwena,b,NikitaMoriakova\\naRadboudUniversityMedicalCenter,DepartmentofRadiologyandNuclearMedicine,Nijmegen,theNetherlands\\nbNetherlandsCancerInstitute,DepartmentofRadiationOncology,Amsterdam,theNetherlands\\nContents\\n20\"\n    },\n    {\n        \"question\": \"Which nonlinearity generally leads to faster convergence in neural networks?\",\n        \"options\": [\n            \"Sigmoid\",\n            \"ReLU\",\n            \"Tanh\",\n            \"None of the above\"\n        ],\n        \"answer\": \"1\",\n        \"context\": \"ReLU nonlinearity generally leads to faster convergence compared to sigmoid\\nor tanh nonlinearities, and it typically works well in CNNs with properly cho-\\nsenweightinitializationstrategyandlearningrate\"\n    },\n    {\n        \"question\": \"What is another way to train a model from cold start, besides using a pre-trained network?\",\n        \"options\": [\n            \"Using a pre-trained network with fine-tuning\",\n            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