A Brief Didactic Theoretical Review on Convolutional Neural Networks, Deep Belief Networks and Stacked Auto-Encoders
Rômulo Fernandes da Costa, Sarasuaty Megume Hayashi Yelisetty, Johnny Marques, Paulo Marcelo Tasinaffo · International Journal of Engineering and Technical Research (IJETR) · 2019
This paper presents a brief theoretical review on deep neural network architectures, deep learning procedures, as well assome of its possible applications.The paper focuses on the most common networks structures: Convolutional Neural Network (CNN), Deep Belief Network (DBN) and Stacked Auto-encoders (SA).The building blocks which enabled the construction of deeper networkssuch as Rectified Linear Unit (ReLU) and softmax activation functions, convolution filters, restricted Boltzmann machines and autoencoders, are explained in the beginning and middle sections of the paper.A few examples of hybrid systems are also presented at the last sections of the paper.The paper concludes with some considerations on the state-of-art work and on the possible future applications enabled by deep neural networks.