Mayurd9/yun

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Mayurd9/CNN-from-scratch

This is the Convolutional Neural Network (CNN) implementation with a single convolution layer from scratch using Numpy library. The image dataset on which classification is done is MNIST. It is able to classify with more than 97% classification accuracy after training for just 1 epoch.

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Mayurd9/CNN-Classification

This repository contains CNN classification code for the MNIST and Fashion MNIST datasets, implemented using the PyTorch library in Python. Approximately 99.4% classification accuracy is achieved for the MNIST dataset, while around 92.3% classification accuracy is achieved for the Fashion MNIST dataset. GPU is utilized for speed improvement.

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Mayurd9/NN-from-scratch

This is the code for a fully connected neural network. The code is written from scratch using Numpy, without using any ready-made deep learning library. In this, classification is done on the MNIST dataset. It is generalized to include various options for activation functions, loss functions, types of regularization, and output activation types.

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Mayurd9/KDE-test

This code is the implementation of the research paper: Statistical change detection for multi-dimensional data. Xiuyao Song, Mingxi Wu, Christopher Jermaine, and Sanjay Ranka. 2007 In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. Association for Computing Machinery,New York,NY,USA,667–676.

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Mayurd9/KIDNEY-MATCHING-PROBLEM

This code is the implementation of the research paper: New insights on integer programming models for the kidney exchange problem. Miguel Constantino, Xenia Klimentova, Ana Viana, and Abdur Rais (Constantino et al). European Journal of Operational Research, 231(1):57–68, 2013. Integer programs in the paper are solved using glpk solver in python.

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