Developing a Convolutional Neural Network for the MNIST Handwritten Digits dataset

The model was trained using a GPU on Amazon Web Services. Instance type: ml.g4dn.xlarge

It's pretty easy to switch between instance types, like this:

More info about Sagemaker pricing here:

• https://aws.amazon.com/sagemaker/pricing/
• https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html
• https://aws.amazon.com/machine-learning/accelerate-machine-learning-P3/
• https://stackoverflow.com/questions/60868257/aws-sagemaker-on-gpu

This repo takes code from the following tutorials:

• Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras

• How to Develop a CNN for MNIST Handwritten Digit Classification

More info about MNIST:

• The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset.

• Excellent results achieve a prediction error of less than 1%. State-of-the-art prediction error of approximately 0.2% can be achieved with large Convolutional Neural Networks. There is a listing of the state-of-the-art results and links to the relevant papers on the MNIST and other datasets on Rodrigo Benenson’s webpage.