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Comparison of Artificial Neural Networks, Support Vector Machines, k-Nearest Neighbours and Convolutional Neural Networks to solve the classification problem of the EMNIST dataset. The objective is to classify handwritten characters as the 26 letters of the english alphabet.

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ML-techniques-study

Comparison of Artificial Neural Networks, Support Vector Machines, k-Nearest Neighbours and Convolutional Neural Networks to solve the classification problem of the EMNIST dataset. The objective is to classify handwritten characters as the 26 letters of the english alphabet.

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The project uses the Julia language and the scikit-learn and Flux packages for ML.

Download this dataset and place on the same folder.

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Comparison of Artificial Neural Networks, Support Vector Machines, k-Nearest Neighbours and Convolutional Neural Networks to solve the classification problem of the EMNIST dataset. The objective is to classify handwritten characters as the 26 letters of the english alphabet.

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