Artificial Neural Networks and Convolutional Neural Networks are becoming common tools for classification and object detection tasks due to their power to learn features without prior knowledge. The networks learn the parameters, weights, and biases through training. This paper proposes a simple Neural Network and Convolutional Neural Network (CNN) to do a classification task. Additionally, the Bayesian neural network work is reproduced to compare the result to my proposed networks and used as a baseline for comparison. The MNIST dataset is used for all experiments. The simple neural networks and the convolutional networks adjust the parameters based on the cost function during training, whereas the Bayesian convolutional neural network updates the parameters based on the backdrop that drives a variational approximation to the true posterior. I trained the two proposed networks by varying the hyperparameters: optimizer, learning rate, regularizers, dropout, epochs, and others. My proposed work gives better classification accuracy, approximately 99 \%, than the previously implemented Bayesian convolutional neural network, but it is difficult to predict how certain the prediction is, which is easy for Bayesian learning.