AN OVERVIEW OF POPULAR DEEP LEARNING METHODS

Musab COŞKUN, Özal YILDIRIM, Ayşegül UÇAR, Yakup DEMIR
2017 European Journal of Technic  
This paper offers an overview of essential concepts in deep learning, one of the state of the art approaches in machine learning, in terms of its history and current applications as a brief introduction to the subject. Deep learning has shown great successes in many domains such as handwriting recognition, image recognition, object detection etc. We revisited the concepts and mechanisms of typical deep learning algorithms such as Convolutional Neural Networks, Recurrent Neural Networks,
more » ... ed Boltzmann Machine, and Autoencoders. We provided an intuition to deep learning that does not rely heavily on its deep math or theoretical constructs. 166 and Autoencoders respectively. We offer our paper in a way that each section can be read independently. Deep learning methods Convolutional Neural Network CNN was firstly introduced by Kunihiko Fukushima [9] . It was later proposed by Yann LeCun. He combined CNN with back-propagation theory to recognize handwritten digits and document recognition [10, 11] . His system was eventually used to read hand-written checks and zip codes. CNN uses convolutional layers and pooling layers. Convolutional layers filter inputs for useful information. They have parameters that are learned so that filters are adjusted automatically to extract the most useful information for a certain task. Multiple convolutional layers are used that filter images for more and more abstract information after each layer. Pooling layers are used for limited translation and rotation invariance. Pooling also reduces the memory consumption and thus allows for the usage of more convolutional layers. Convolution Operation Convolution is just a mathematical operation that describes a rule of how to mix two functions and produces a third function. This third function is an integral that expresses the amount of overlap of one function as it is shifted over the other function. In other words, an input data and a convolution kernel are subjected to particular mathematical operation to generate a transformed feature map. Convolution is often interpreted as a filter, where the kernel filters the feature map for information of a certain kind. Convolution is described formally as follows: (1) CNN typically works with two-dimensional convolution operation as summarized in Fig. 2 . The leftmost matrix is input data. The matrix in the middle is convolution kernel and the rightmost matrix
doi:10.23884/ejt.2017.7.2.11 fatcat:l3oygb3ljbgzvca7kodmziu3me