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JavaFx Application for Convolutional Network to perfom Image Classification using Softmax Output Layer, Back Propagation, Gradient Descent, Partial Derivatives, Matrix Flattening, Matrix Unfolding, Concurrent Task, Performance Histogram, Confusion Matrix

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p-dirac/javafx-convolution-network

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JavaFx Convolutional Network

This project demonstrates the implementation of a JavaFX front end and a convolutional neural network (CNN) back end. The front end interface allows the user to create various network scenarios without modifying the code. The back end code includes network layers, activation functions, a matrix library, and json utilities.

See JavaFX-Convolutional-Network.pdf for more information.

Keywords: JavaFX Application, Convolutional Neural Network, Image Classification, Softmax Output Layer, Back Propagation, Gradient Descent, Partial Derivatives, Matrix Flattening, Matrix Unfolding, Concurrent Task, Performance Histogram, Confusion Matrix