“Investigating the Influence of Ages on the Preparation and Validation Performance of MLP

Abstract This research explores the impact of varying the number of training epochs on the performance of a multilayer perceptron (MLP) applied to MNIST handwritten digit classification. MNIST, a benchmark dataset in machine learning, comprises grayscale images of digits 0–9. The investigation employs PyTorch as the deep learning framework, delving into the intricacies of MLP training through an iterative process. The study systematically adjusts the number of training epochs to probe the hyperparameter's influence on MLP convergence and generalization capabilities. Conducted experiments involve training the MLP with different epoch counts while monitoring training and validation accuracies. Results are meticulously analyzed to unveil patterns in model performance, with a focus on identifying optimal epochs that strike a balance between underfitting and overfitting. The research yields valuable insights into the optimal training duration for MLPs in MNIST digit classification. This newfound knowledge offers practical guidance for practitioners in selecting appropriate hyperparameters during MLP training. The implications extend to enhancing model performance and generalization for analogous image classification tasks, contributing to the broader field of machine learning.