Two MNIST classification python notebooks, one using classical ML techniques and the other using a Keras neural network.
IMPORTANT NOTE: The MNIST dataset is large and so cannot be uploaded to this repo. you can download it here http://yann.lecun.com/exdb/mnist/
Then simply change the #import file destination to make use of these note books.
MNIST Classification Notebook: Goes through some classical techniques for classification, K-nearest-neighbours and random forest. Tests are run on the applicability of different values for K number of neighbours leading to the following accuracy results:
Various dimensionality reduction techniques and which when tested with the optimal KNN results achieve these results:
MNIST Convolution Neural Network: This covers the use of the Keras library for Neural Networks. I won't go into the details here but with fairly simple CNN implementation I achieved this confusion matrix at 98.8% Accuracy:
For further regularisation I implemented a data generating process which moves/distorts images in the dataset randomly, the model then achieved 99.1% accuracy with the following confusion matrix:
The ideology behind tests/implementation and results are more clearly seen in the notebooks so I reccomend you give it a look!