Open AccessDeep Learning – A Review
Author(s) -
Vamsi Madhav Kota,
Vimal Kumar,
C. Bharatiraja
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/912/3/032068
Subject(s) - deep learning , artificial intelligence , computer science , convolutional neural network , domain (mathematical analysis) , perceptron , artificial neural network , curiosity , architecture , recurrent neural network , machine learning , data science , psychology , mathematical analysis , social psychology , art , mathematics , visual arts
In recent years, tech giants in various parts are showing Curiosity on Artificial Intelligence by investment on the project that can be a game-changer for both corporate and researchers. A company such as Google, Baidu, and Yandex have already started their multimillion-dollar project in this pitch. This article presents the latest progress and also tries to paint a predictive picture of future research directions and developments in the domain of deep learning. Each of the said research directions and avenues is analyzed and summarized in a brief yet concise manner in this article. Initially, an outline of the three elementary models of deep learning that including multilayer perceptions and perceptrons, convolutional neural networks and recurrent neural networks. Building on the bases of foundation, further analyses of the emerging new types of convolutional neural networks and recurrent neural networks are also undertaken in this current study. This article then summarizes deep learning and its applications in the domain of artificial intelligence, counting speech processing, computer vision, and natural language processing. Finally, the purpose of deep learning is discussed. The current article also delves a little deeper into the inner workings of the neural networking architecture associated with object detection and computer vision.