Efficient Deep Neural Network Training Techniques for Overfitting Avoidance

Abstract

A deep learning neural network’s ultimate goal is to produce a model that does well on both the training data and the incoming data that it will use to make predictions. Overfitting is the term used to describe how successfully the prediction model created by the machine learning algorithm adapts to the training data. When a network is improperly adapted to a limited set of input data, overfitting occurs. In this scenario, the predictive model will be able to offer very strong predictions on the data in the training set, and will also capture the generalizable correlations and the noise produced by the data, but it will predict poorly on the data that it has not yet seen during his learning phase. Two methods to lessen or prevent overfitting are suggested in this publication among many others. Additionally, by examining dynamics during training, we propose a consensus classification approach that prevents overfitting, and we assess how well these two types of algorithms function in convolutional neural networks. Firstly, Early stopping makes it possible to store a model’s hyper-parameters when it’s appropriate. Additionally, the dropout makes learning the model more challenging. The fundamental concept behind dropout neural networks is to remove nodes to allow the network to focus on other features which reducing the model’s loss rate allows for gains of up to more than 50%. This study looked into the connection between node dropout regularization and the quality of randomness in terms of preventing neural networks from becoming overfit.

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