Application of Convolutional Neural Network.

Running head: APPLICATION OF CONVOLUTIONAL NEURAL NETWORK
Application of Convolutional Neural Network
Name of the Student
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APPLICATION OF CONVOLUTIONAL NEURAL NETWORK1
Introduction
In machine learning’ field, Convolutional Neural Network or CNN is Deep Learning
algorithm that are mainly used in pattern classification, image and recognition. The algorithm is
provided with input image along with assigning importance to the variables. This includes
different learnable weights or biases of various objects that are present in input image and are
able in differentiating one from other.
In pre-processing stage the CNN requires much less time compared with other
traditional classification algorithm. In case of primitive algorithms filters needed to be hand-
engineered by providing enough training, where as in case of Convolutional Neural Network
the algorithm had the ability to learn about the filters for classification. Through the use of the
convolutional neural networks, it can help in Object detection, face recognition with video
analysis by using the segmentation, pattern recognition methods.
Problem definition
For this project the MNIST handwritten digit dataset is chosen. The dataset contains
detailed data about 70,000 images of handwritten digits. The dataset contains images of different
digits that are helpful in training the Convolutional Neural Network and testing the same
network. Out of the 70000 images 42,000 scanned images of digits are utilized for training the
network and remaining 28000 for the testing of the developed CNN model. All the images in the
dataset are utilized in the experiment are grayscale images with size of 28×28 pixel. The dataset
includes 784-dimensional vector about the images pixels as every image is of 28×28 pixels.

APPLICATION OF CONVOLUTIONAL NEURAL NETWORK2
Justification for Using CNN
CNN is one of variants of the neural networks that is used heavily within Computer
Vision’s field. It derives the name from the hidden layers which are in it. CNN’s hidden layers
consist of pooling layers, normalization layers, fully connected layers and convolutional layers
(Dong, Loy & Tang, 2016). It means that in spite of using functions of normal activation,
pooling and convolution functions are used in as the activation functions. Convolution operates
over two signals of 1D or two images of 2D, one as input signal and other as filter on input
image, which produces output image. Pooling is discretization process that is based on sample.
Objective is in down-sampling input representation, which reduces the dimensionality as well as
allows for assumptions that are made about the features that are contained within sub-regions
binned (Jin et. al., 2017). CNN is deep neural network that includes hidden layers which have
pooling and convolution functions along with activation function to introduce non-linearity.
Figure1: Count of images with different labels