Abstract
Development of efficient neural network classifier topology involves preprocessing of training data to reduce noise and discrepancies, utilizing a number of hidden layers, choice of correct activation functions etc. In this research we propose using a combination of first order Stochastic Gradient Descent (SGD) optimizers for Artificial Neural Networks (ANNs) with Deep Learning (DL) and Deep Learning Multilayered Perceptron DLMLP with Back Propagation (BP) for classification of multiclass real datasets with large dimensions. DLMLP topologies require varying the number of hidden layers and their neurons, the development of specific activation and loss functions, batch processing, normalization, implementation of different gradient methods, approaches to avoid overfitting/underfitting as well as evaluation of training and classification results. Our focus is on optimization of stochastic objectives and parameters as well as implementation of different activation functions. K-fold cross validation is utilized to compare the performance of each model with the respective set of parameters. Experiments shown here include Cover Type, Mushrooms, EEG and Plastic Detection datasets where the loss and accuracy metrics are compiled and visualized.