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IJSTR >> Volume 9 - Issue 6, June 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Significance Of Epochs On Training A Neural Network

[Full Text]



Saahil Afaq, Dr. Smitha Rao



Hyperparameters, epochs, nodes, hidden layers, Deep Neural Network (DNN), Architecture.



Deep neural network (DNN), has shown an incredible success in the field of computer vision and in tasks such as classification, facial detection etc. But, accuracy of a model depends on a large number of parameters such as weights, bias, number of hidden layers, different kinds of activation function and hyperparameters. Epochs is a form of hyperparameter which plays an integral part in the training process of a model. The total number of epochs to be used help us decide whether the data is over trained or not. Recently, the performance of deep neural networks, have been improved by making use of pre-trained network architectures, and by the introduction of GPU-based computation and now recently we are even on the verge of training the models on TPU chips. However, there are many problems in the field of Deep Neural Network which concerns the training, back propagation, and customizing of the hyperparameters.



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