soumita0210/Unsupervised-Learning-using-Autoencoders

K-Means and Gaussian Mixture Model ( GMM ) have been used for clustering the MNIST Fashion Dataset. To address the problem of the large number of features we used Autoencoders for dimension reduction. Autoencoders are used to compress the images in the dataset, however it results in a lossy compression. However, the reduced dimensions of the images results in K-Means and GMM running more efficiently. Results have been compared for all the experiments using the above three concepts.

Jupyter Notebook