Autoencoding with K-Means

The MNIST dataset contains images of handwritten digits from 0 to 9. Using the machine learning K-Means algorithm to cluster the MNIST dataset achieves poor results. This experiment seeks to improve and measure the accuracy by using an autoencoder to massage the input data prior to K-Means. To measure the results a purity score will be generated and a confusion matrix will display the distribution of the clusters. The notebook is available on GitHub Generate…


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