Tag: GNBC

• Unsupervised Learning of Gaussian Mixture Models With the EM Algorithm (13 Dec 2019)
  The Expectation-Maximization algorithm is a powerful iterative method for calculating maximum likelihood estimates from unlabeled data. In this article, we will be exploring its use in Gaussian mixture models to perform the task of clustering with the Scikit-learn digits dataset. The performance of the EM-algorithm is then compared to a vanilla k-Means implementation provided in Scikit-learn.
• Comparing the Performance of Various Classifiers on the Scikit-learn Digits and MNIST Datasets (06 Dec 2019)
  In this article we will be implementing several classifiers to predict the class labels of the Scikit-learn digits and MNIST_Light datasets.