![The effect of activation function choice on the performance of convolutional neural networks](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBaFlQIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--3c4465c6d4816c3e05d53dc6b7ef0e66ca05a612/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lKYW5CbFp3WTZCa1ZVT2d0eVpYTnBlbVZKSWcwMk1EQjROakF3UGdZN0JsUT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--52131d7e1d9fc5c464ef2fd793e4b0873b571622/pexels-tara-winstead-8386440.jpeg)
With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.
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