The Modified National Institute of Standards and Technology (MNIST) dataset is a very well-known dataset of handwritten digits {0,1,2,3,4,5,6,7,8,9} used to train and test classification models.
A classification model is a model that predicts the probabilities of observing a class, given an input.
Training is the task of learning the parameters to fit the model to the data as well as we can so that for any input image, the correct label is predicted. For this training task, the MNIST dataset contains 60,000 images with a target label (a number between 0 and 9) for each example.
To validate that the training is efficient and to decide when to stop the training, we usually split the training dataset into two datasets: 80% to 90% of the images are used for training, while the remaining 10-20% of images will not be presented to the algorithm for training but to validate that the model generalizes well on unobserved data.
There is a separate dataset that the algorithm should never...
