{"url":"/dataset/mnist","name":"MNIST","full_name":null,"description_markdown":"The **MNIST** database (**Modified National Institute of Standards and Technology** database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.\r\n\r\nSource: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)\r\nImage Source: [https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png](https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png)","description_withheld":null,"homepage":"http://yann.lecun.com/exdb/mnist/","introduced_date":"1998-11-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/gradient-based-learning-applied-to-document","title":"Gradient-based learning applied to document recognition","first_author":"Y. 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