What is Recurrent dropout in neural network

This recipe explains what is Recurrent dropout in neural network

Recipe Objective - What is Recurrent dropout in neural network?

Recurrent dropout technique is used to improve the performance and the generalization power of the recurrent networks. Recurrent dropout is used to fight overfitting in the recurrent layers. Recurrent dropout helps in regularization of recurrent neural networks. As recurrent neural networks model sequential data by the fully connected layer, dropout can be applied by simply dropping the previous hidden state of a network. Overfitting in neural networks models prevent the models to achieve high accuracy and it decreases model performance on unseen data in real-time. Recurrent dropouts are widely used in Long short-term memory and GRU layers to fight overfitting in the neural network model so that model accuracy can be achieve solving business problem.

This recipe explains what is recurrent dropout, how it is beneficial for neural network models and how it can be executed.

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Explanation of Recurrent dropout.

Neural Network models often suffer from overfitting especially when the number of network parameters are large and the amount of training data is small. This has led to a lot of research directed towards improving the generalization ability. Recurrent dropout is the widely used in industry for correcting overfitting in model building stage. Recurrent dropouts method is specifically tailored for gated architectures.

Recurrent dropout can be executed using the Keras framework easily.

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I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

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