Comparative Study on the Deep Learning Algorithms

Abstract

Abstract: Deep Learning methods have paved the way for elevating the future technology that is capable of changing the world. In modern times, size of data is increasing with the level of application. Deep learning enables the huge dataset to process the highly optimized algorithms with high accuracy as well as within low time. The network architecture of deep learning works similar to human brain nerves. The network accepts the input dataset and convert the data into matrix form that passed through multiple layers in which, each layer upgrade the data to deliver the prediction or classification at the end. Researchers explored the numerous deep learning models that portrayed an inspiration for developers and benefitted in the field of voice recognition, language translation, image categorization, stock market prediction etc. The concern behind the model is to effectively resolve the numerous tasks which need to distributed representation and human intelligence. The highly advanced processors like CPU and GPU has too enhanced the deep learning application through fast matrix calculations and image processing. We will take the sample of wind dataset and used it for comparing the different Deep Neural Network (DNN) artificial algorithm. Keywords: Analysis, comparison, deep learning, training, prediction.