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Regular version of the site
Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Valeriya Ignatovskaya
Improving Performance of Recurrent Neural Networks
Mathematics
(Bachelor’s programme)
2018
The paper deals with the problem of exploding and vanishing gradients during the training of

recurrent neural networks. Several existing approaches to solution of this problem are described. In particular, unitary recurrent neural networks and ways to implement them are thoroughly explored. In addition, own idea of implementing a unitary recurrent neural network is being developed. The effectiveness of each method of implementation is experimentally demonstrated

on topical problems from different areas.

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