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Activation Functions and Training Algorithms for Deep Neural Network

Gayatri Khanvilkar, Deepali Vora,

Department of Information Technology Vidyalankar Institute of Technology Mumbai, India

Machine Learning is a Field of computer science that gives the computer the ability to learn without being explicitly programmed. It is core subpart of artificial intelligence. Whenever new data exposed, computer programs, are enabled to learn, grow, change, and develop by themselves. Machine learning is study and construction of algorithms that learn and do the prediction based on data. Deep learning is nothing but subfield of machine learning. Structure and function of human brain inspire deep learning. ‘Deep learning' name is used for stack neural network. The deep neural network is an Artificial Neural Network with number of hidden layers and hence different from the normal artificial neural network. Supervised and unsupervised manner can train it. Training of such Deep neural network is difficult also it mainly faces two challenges, i.e. over fitting and computation time. Deep neural network train with the help of training algorithms and activation function. So, in this paper mostly used Activation Function (Sigmoid, Tanh and ReLu) and Training Algorithms (Greedy layer-wise Training and Dropout) are analysed and according to this analysis comparison of activation functions and training algorithms are given

Gayatri Khanvilkar, Deepali Vora (2018). Activation Functions and Training Algorithms for Deep Neural Network. International Journal of Computer Engineering In Research Trends, 5(4), 98-104. Retrieved from

Keywords : Deep Neural network, Activation Functions, Vanishing gradient, Greedy Algorithm, Dropout Algorithm

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