Recognizing handwritten digits with an ANN

A handwritten digit is any of the 10 Arabic numerals (0 to 9), written manually with a pen or pencil, as opposed to being printed by a machine. The appearance of handwritten digits can vary significantly. Different people have different handwriting, and – with the possible exception of a skilled calligrapher – a person does not produce identical digits every time he or she writes. This variability means that the visual recognition of handwritten digits is a non-trivial problem for machine learning. Indeed, students and researchers in machine learning often test their skills and new algorithms by attempting to train an accurate recognizer for handwritten digits. We will approach this challenge in the following manner:

• Load data from a Python-friendly version of the MNIST database. This is a widely used database containing images of handwritten digits.
• Using the MNIST data, train an ANN in multiple epochs...