MNIST Handwritten Digit Classification Using Neural Network

Abstract: Handwritten digit classification is a fundamental problem in the field of machine learning and computer vision. The MNIST dataset, consisting of 60,000 training images and 10,000 test images of handwritten digits, has been widely used as a benchmark for evaluating classification algorithms. In this study, we propose a neural network-based approach for classifying the MNIST digits. Our model consists of multiple layers, including an input layer, one or more hidden layers, and an output layer. We preprocess the images by normalizing the pixel values and convert them into a suitable format for the neural network. The model is trained using the training images and their corresponding labels, adjusting the weights of the connections to minimize the difference between the predicted outputs and the actual labels. We evaluate the performance of the model using metrics such as accuracy on the test images. Our results demonstrate the effectiveness of the neural network approach in accurately classifying handwritten digits. This research contributes to the advancement of digit recognition techniques and provides insights into the application of neural networks in image classification tasks