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MNIST Written Character Classification with a Multi-Layer Perceptron

14/04/2022 Client: dostdar.cui Deadline: 6 Hours

1) Write a brief introduction that introduces
1) Explains what MNIST is about and what its contents are, what relevant size characteristics are
2) Explain why MNIST is more challenging than the Iris dataset.
3) Briefly discuss key algorithm performances using the listing on the data set homepage and explain what can be expected for the following experiment.
2) Implement and document a multi-layer perceptron and the backpropagation training algorithm in Matlab
Build your code up systematically step by step and test. Provide evidence of that process.
3) Realize and describe an experiment in Matlab that evaluates the classification error rate for MLP on the MNIST dataset. Use appropriate illustrations and diagrams as well as statistics.
1) Make sure you have one successfully learning parameter set first, and start to explore systematically from there. Pay particular attention to finding an appropriate learning rate first.
2) This experiment can be conducted without a full back-propagation implementation as long as the forward propagation and the learning of the output layer works, although results will vary from the intended experiment.
4) Bonus points for additional features of MLP or experiment, see above.
5) Write a brief conclusion on the results and compare to results documented for other algorithms as well as MLP configurations on the data set homepage. Explain possible current limitations of your solutions and possible further strategies to improve on the results

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