Convolutional Neural Networks and Pattern Recognition: Application to Image Classification

This research study focuses on pattern recognition using
convolutional neural network. Deep neural network has been
choosing as the best option for the training process because
it produced a high percentage of accuracy. We designed
different architectures of convolutional neural network in
order to find the one with high accuracy of image
classification and optimum bias. We used CIFAR-10 data
set that contains 60000 Images to train our model on
architectures. The best architecture was able to classify
images with 95.55% of accuracy and an error of 0.32%
using cross validation method. We note that, the numbers of
epoch while running the model and the depth of the
architecture are factors that contributed to get this
performance.