2018/05/12 by Kumarjit Pathak, G Prabhukiran, Pathak, Kumarjit +5
Computer Science · #FOS: Computer and information sciences #Intuitionistic Fuzzy Systems Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Online Learning and Analytics
paper · pdf · doi:10.48550/arxiv.1805.04754
openalex publication_date 2018/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
High volume of data, perceived as either challenge or opportunity. Deep learning architecture demands high volume of data to effectively back propagate and train the weights without bias. At the same time, large volume of data demands higher capacity of the machine where it could be executed seamlessly. Budding data scientist along with many research professionals face frequent disconnection issue with cloud computing framework (working without dedicated connection) due to free subscription to the platform. Similar issues also visible while working on local computer where computer may run out of resource or power sometimes and researcher has to start training the models all over again. In this paper, we intend to provide a way to resolve this issue and progressively training the neural network even after having frequent disconnection or resource outage without loosing much of the progress