The convolution neural network (CNN) has achieved state-of-the-art performance in many
computer vision applications eg, classification, recognition, detection, etc. However, the
global optimization of CNN training is still a problem. Fast classification and training play a
key role in the development of the CNN. We hypothesize that the smoother and optimized
the training of a CNN goes, the more efficient the end result becomes. Therefore, in this
paper, we implement a modified resilient backpropagation (MRPROP) algorithm to improve …

The convolution neural network (CNN) has achieved state-of-the-art performance in many
computer vision applications eg, classification, recognition, detection, etc. However, the
global optimization of CNN training is still a problem. Fast classification and training play a
key role in the development of the CNN. We hypothesize that the smoother and optimized
the training of a CNN goes, the more efficient the end result becomes. Therefore, in this
paper, we implement a modified resilient backpropagation (MRPROP) algorithm to improve …