Multi-layer Neural Network Perception for Large Data Prediction using R Programming
Abstract
This review article primarily focus on neural network perceptionitechniques for large data analysis using r programming on secondary data set. Although there is a large confusion while selctioning an appropriate analysis tools for the newer researchersr when there was a large set of associative variables like CTG data sets having 2126 observations in 22 attributing variables of numeric types. The last variable NSP with categorical type 1,2 and 3 stores best, mediam and least properties. This article attempts to explore the detail analysis using the concept of multi-ilayered neural networks procedures for sample CTG data sets where each step to reach conclusion were sufficiently explained. Its purpose is to explain the simplest form of analysis when researcher meets large number of dependent and independent varialbes research data structure using software R whose results are summarized and inferenced with sufficiently with interemediate results and conclusions were drawn using appropriate graphical interpretation were analyze using 8 hidden layers with activation of relu of 21 independent variables and loss is calculated with different epoch of categorical crossentropy . The each model value is evaluated with confusion matrixed prediction. The model is considered as best fit when the two-line graphs were in constant in more iterations. The tenser flow package is widely used in image recognition, computer vision, speech / sound recognition and time series analysis and many more.
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