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

“Investigating the Influence of Ages on the Preparation and Validation Performance of MLP | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article “Investigating the Influence of Ages on the Preparation and Validation Performance of MLP SHAFIQUL-ABIDIN SHAFIQUL-ABIDIN, MOHD. IZHAR, RUCHI SAWHNEY, Haider Abbas Haider This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3848073/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Multilayer perceptron (MLP) PyTorch MNIST handwritten digit classification training epochs deep learning neural network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3848073","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281659008,"identity":"20d7da47-79b7-4872-868a-a2ed0ce816cc","order_by":0,"name":"SHAFIQUL-ABIDIN SHAFIQUL-ABIDIN","email":"","orcid":"","institution":"Aligarh Muslim University","correspondingAuthor":false,"prefix":"","firstName":"SHAFIQUL-ABIDIN","middleName":"","lastName":"SHAFIQUL-ABIDIN","suffix":""},{"id":281659009,"identity":"696e965c-f928-4627-b2ce-5bc0c6ccd4d8","order_by":1,"name":"MOHD. 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