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Stochastic Gradient Descent Algorithms performance improvement with Convolutional Neural Networks

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Study area: Computer Science

Research Area: Improving the accuracy rate and training time of Stochastic Gradient Descent Algorithm on Convolutional Neural Networks.

I have already got proof of concept program code and favourable results in this research area.

Chapter 1: Big Data, Deep Learning, Neural Networks, Convolutional Neural Networks,

Chapter2 : Optimization Algorithms, Gradient Descent Algorithms, Stochastic Gradient Descent Algorithm

Chapter 3: I already have a published conference paper which would form basis of chapter 3. It involves addition of a strategic weight refinement maneuvre to Stochastic Gradient Descent Algorithms for ConvolutionalNeuralNetworks. This maneuvre will act as a guide to SGD Algorithms which results in improved accuracy rates.

Chapter 4: the same maneuvre is now parallelized and applied. Discuss drawbacks of previous chapter~ while improving accuracy it makes the training time longer. Solution is to parallelize thr strategic weight refinement maneuvre that I discussed in chapter 3. For reference and help ij writing this chapter , I will provide an additional paper which was applied to logistic regression and you can refer same concept being applied to Convolutional Neural Networks

Chapter 5: Overall discussions and benefit of approach discussed in the thesis. Closing remarks and conclusion.

References.

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