Strategies for Boosted Learning Using VGG 3 and Deep Neural Network as Baseline Models

Abstract

Deep Convolutional Neural Network learns with various levels of abstraction and has made a revolution among learning algorithms. Deep Learning currently plays a vital role in object classification, natural language processing, genetics, and drug discovery. The deep learning unravels the patterns by computing gradients to minimize the loss function and based on it the internal parameters are tuned to compute the layer-wise representation. The Deep Convolutional Neural Network has revolutionized computer vision and image processing. The work cross dissects the deep convolutional neural network to light its learning mechanism and extensively experiments parameter tuning to facilitate intelligent learning. The work comes up with a roadmap to build and train a deep convolutional neural network after extensive experimentation using CIFAR 10 dataset. The work also comes up with near-optimal hyperparameters that effectively generalize the learning of Neural Network. The performance of the Deep Neural Network is also evaluated with hypertension dataset gathered from the health department. On experimentation, could be inferred that the proposed approach gives comparatively higher precision. The accuracy of the proposed approach is found to be 90.06%.