  Deep Learning (DL) is a machine learning procedure for artificial
intelligence that analyzes the input data in detail by increasing neuron sizes
and number of the hidden layers. DL has a popularity with the common
improvements on the graphical processing unit capabilities. Increasing number
of the neuron sizes at each layer and hidden layers is directly related to the
computation time and training speed of the classifier models. The
classification parameters including neuron weights, output weights, and biases
need to be optimized for obtaining an optimum model. Most of the popular DL
algorithms require long training times for optimization of the parameters with
feature learning progresses and back-propagated training procedures. Reducing
the training time and providing a real-time decision system are the basic focus
points of the novel approaches. Deep Extreme Learning machines (Deep ELM)
classifier model is one of the fastest and effective way to meet fast
classification problems. In this study, Deep ELM model, its superiorities and
weaknesses are discussed, the problems that are more suitable for the
classifiers against Convolutional neural network based DL algorithms.
