Scalable Neural Network Algorithms for High Dimensional Data

Abstract

The boundary for machine learning engineers lately has moved from the restricted data to the algorithms' failure to involve every one of the data in the time permitted. Due of this, scientists are presently worried about the adaptability of machine learning algorithms notwithstanding their exactness. The key to success for many computer vision and machine learning challenges is having big training sets. A few published systematic reviews were taken into account in this topic. Recent systematic reviews may include both more recent and older research on the subject under study. Thus, the publications we examined were all recent. The review utilized information that were gathered somewhere in the range of 2010 and 2021. System: In this paper, we make a modified brain organization to eliminate possible components from extremely high layered datasets. Both a totaled level and an exceptionally fine-grained level of translation are feasible for these highlights. It is basically as easy to grasp non-straight connections as it is a direct relapse. We utilize the method on a dataset for item returns in web based shopping that has 15,555 aspects and 5,659,676 all out exchanges. Result and conclusion: We compare 87 various models to show that our approach not only produces higher predicted accuracy than existing techniques, but is also interpretable. The outcomes show that feature selection is a useful strategy for enhancing scalability. The method is sufficiently abstract to be used with many different analytics datasets