  Nowadays, deep learning can be employed to a wide ranges of fields including
medicine, engineering, etc. In deep learning, Convolutional Neural Network
(CNN) is extensively used in the pattern and sequence recognition, video
analysis, natural language processing, spam detection, topic categorization,
regression analysis, speech recognition, image classification, object
detection, segmentation, face recognition, robotics, and control. The benefits
associated with its near human level accuracies in large applications lead to
the growing acceptance of CNN in recent years. The primary contribution of this
paper is to analyze the impact of the pattern of the hidden layers of a CNN
over the overall performance of the network. To demonstrate this influence, we
applied neural network with different layers on the Modified National Institute
of Standards and Technology (MNIST) dataset. Also, is to observe the variations
of accuracies of the network for various numbers of hidden layers and epochs
and to make comparison and contrast among them. The system is trained utilizing
stochastic gradient and backpropagation algorithm and tested with feedforward
algorithm.
