[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120403-en":3,"doc-seo-120403-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120403,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Network Design for Efficient Distributed Machine Learning Process - Graduate Project","This graduate project focuses on improving distributed machine learning efficiency through network design. It develops three network design algorithms aimed at addressing challenges created by large data sets and machine learning requirements. The work emphasizes distributed computing with topology guidance, where communication speed, information exchange smoothness, and collaborative intelligence influence performance. The document explores data center placement, communication delays, and algorithmic strategies such as gradient descent, presenting results and conclusions to support future enhancements.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nNetwork Design for Efficient  \nDistributed Machine Learning Process  \nA graduate project submitted in partial fulfillment of the requirements for the degree of Master  \nof Science in Computer Engineering  \nBy  \nYesasvini Sai Meghana Chikkam  \nCopyright by Yesasvini Sai Meghana Chikkam 2024  \nThe graduate project of Yesasvini Sai Meghana Chikkam is approved:  \n\n| Dr. Brad Jackson | Date |\n| --- | --- |\n| Dr. Sevada Isayan | Date |\n\nDr. Myung Cho, Chair  \nDate  \nCalifornia State University, Northridge  \nPREFACE  \nIn this document, we dive deep into the innovation of three network design algorithms developed during my research. The combination of expanding data sets with advanced machine learning techniques puts forward many hurdles in this field of study. Looking to the future, my exploration lead me to the path for enhanced machine learning within a distributed framework of data set.  \nThe era of distributed computing serves as the focus, with guidance of network topology making machine learning techniques more efficient. The computers that we depend on are not just their own individual stations, but are important parts ofa wide network, linked in the form on a network which requires communication. The speed of communication, the seamlessness with which they exchange information amongst each other, and the collaborative intelligence's shared are set to shape the future of machine learning.  \nAs we proceed, this document will guide us through the complexities of machine learning techniques across distributed systems. We'll read more into the placement of data centers, the constant problems faced due to communication delays, and the various strategies and algorithms, such as gradient descent, made to improvise machine learning operations. The insights we gain in this project, have the potential to revolutionize industries and elevate research efforts.  \nThrough this journey of discovery, the goal transcends is not to answering the existing questions, but to ignite curiosity for new questions and solutions. In this project, algorithms were developed to improvise efficiency of distributed machine learning techniques.  \nACKNOWLEDGEMENTS  \nThe project opportunity I had with Dr. Myung Cho as a Graduate Student was a great chance for learning and development. I feel incredibly fortunate to have been given the opportunity to be apart ofit, which is why I consider myself a lucky individual. I am also grateful for having a chance to work with so many wonderful other students working under the same professor.  \nSpecial thanks are due to my committee chair Dr. Myung Cho whose help, guidance, stimulating suggestions, and encouragement helped me during the entire internship period. I would also like to acknowledge with much appreciation his crucial role in discussing and developing algorithms for this project.  \nI express my deepest thanks to the committee members Dr. Sevada Isayan and Dr. Brad Jackson for the time spent proofreading, correcting my mistakes, giving necessary advice and guidance at all times of the project. At this moment, I wish to express my heartfelt appreciation for their valuable contributions.  \nI also want to convey my thanks to Dr. Ashley Geng. , Department Chair of Computer Engineering Dept who stood as a constant support during my entire under graduation course period.  \nI view this opportunity as a significant milestone in my career growth. I am committed to utilizing the skills and knowledge I've acquired to the best of my ability, and I will persistently work on enhancing them to achieve my career goals.  \nTABLE OF CONTENTS  \nCopyright page……………………………………………………………………………………ii  \nSignature Page…………………………………………………………………………………... iii  \nPreface…………………………………………………………………………………………... iv  \nAcknowledgements………………………………………………………………………………. v  \nList of Tables & Figures………………………………………………………………………... vii  \nAbstract……………………………………………………………………………………….... viii  \nChapter 1: INTRODUCTION……………………","cbCaicPNN6i7KOis","https://ap.wps.com/l/cbCaicPNN6i7KOis","pdf",975769,1,37,"English","en",105,"# Preface\n# Acknowledgements\n# List of Tables & Figures\n# Abstract\n# Chapter 1: Introduction\n## Machine Learning on Distributed Networks\n## Rationale of Work\n## Problem Statement\n## Objectives\n# Chapter 2: Previous Work\n# Chapter 3: Methodology\n## Project Details\n## Project Methodology\n## Algorithms and Explanation\n# Chapter 4: Results\n## Numerical Conclusions\n## Numerical Experiments\n# Chapter 5: Conclusion\n## Future Scope of Work\n# References\n# Appendix","[{\"question\":\"What problem does the project target in distributed machine learning?\",\"answer\":\"It targets efficiency challenges in distributed machine learning caused by expanding data sets and constraints of distributed computing, especially communication delays and networking topology effects.\"},{\"question\":\"What are the main components of the project workflow?\",\"answer\":\"The project outlines the background and previous work, then describes methodology including project details, project methodology, and the developed algorithms, followed by results from numerical experiments and conclusions.\"},{\"question\":\"Which algorithms or techniques are referenced as part of improving learning operations?\",\"answer\":\"The document highlights network design algorithms and also discusses machine learning strategies such as gradient descent to improve distributed machine learning operations.\"}]","Network Design for Efficient Distributed Machine Learning Process - 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