Comparative Analysis of Various Neural Networks with Dropout Technique

Abstract

In recent years, neural networks have plays a vital role. One key technique that improves the neural network performance is the dropout technique, which helps to prevent overfitting. This paper presents a comparative study of various neural network architectures with dropout. We discuss different datasets used, specific models implemented, and also analysis the performance of the dropout on various neural network. This analysis Convolutional Neural Networks, Recurrent Neural Networks and Multilayer Perceptrons. Finally, we determine which neural network architecture benefits the most from the dropout technique based on empirical results.

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