Comparison of the Error Rates of MNIST Datasets Using Different Type of Machine Learning Model
Md Suhel Rana, Md Humayun Kabir, Abdus Sobur · Zenodo (CERN European Organization for Nuclear Research) · 2023
The MNIST dataset is a popular benchmark dataset in the field of machine learning and computer vision. The dataset has a training set of 60,000 examples, and a test set of 10,000 examples where the digits have been centered inside 28x28 pixel images. The dataset is commonly used for image classification tasks, where the goal is to train a model to correctly identify the digit represented in each image. The MNIST dataset has been widely used in academic research, with many researchers using it to develop and test new machine learning algorithms. It has also been used in industry, with many companies using it to train and evaluate image recognition systems. The MNIST dataset is an important resource for the machine learning and computer vision communities and has played a significant role in the development of these fields. The MNIST dataset has the advantage of striking a good balance in terms of the scope of the issue. The photos are only available in 10 different classifications and are only 28x28 pixels in size. However, just because the images are small does not necessarily suggest that the data set's numbers do not have a significant amount of variation. It should come as no surprise that some of the digits are challenging for a human to accurately classify. The composite average of the class the classifier is most likely to choose is displayed after a selection of photos that are likely to be extremely challenging to identify with a classifier. These photographs are challenging because they remarkably resemble the typical (or another prevalent) image of another.