Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks

Exploring foci of: International Journal of Advanced Computer Science and Applications • Vol 13 • No 11 Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks January 2022 • Ubaid M. Al‐Saggaf, Abdelaziz Botalb, Muhammad Faisal, Muhammad Moinuddin, Abdulrahman U. Alsaggaf, Sulhi A. Alfakeh Convolutional Neural Network (CNN), a type of Deep Learning, has a very large number of hyper-meters in contrast to the Artificial Neural Network (ANN) which makes the task of CNN training more demanding. The reason why the task of tuning parameters optimization is difficult in the CNN is the existence of a huge optimization space comprising a large number of hyper-parameters such as the number of layers, number of neurons, number of kernels, stride, padding, rows or columns truncation, parameters of the backpropa… Open Article Page

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