Enhancing Neural Network Models for MNIST Digit Recognition

Using the MNIST dataset, a standard in computer vision, this study tries to improve neural networks' digit recognition ability. Focusing on elements such as neural network architecture, hyperparameters (dropout rate and training epochs), and their effect on digit identification, it examines a variety of methodologies and strategies. The study identifies hyperparameter settings that significantly increase accuracy. Results indicate that the model with the highest accuracy, ranging from 80.96% to 98.67%, used the Adam optimizer, four hidden layers with Dropout, 0.1 learning rate, and 23 epochs. These discoveries improve MNIST digit recognition and have wider ramifications, including those for document analysis and financial transactions.