The deep learning abilities of Convolutional Neural Networks have proven to be a boon to many industries, from facial recognition in security to self-driving automobiles. Convolutional Neural Networks differ from traditional feed-forward neural networks in that they feature ‘convolutional’ and ‘pooling’ layers. The convolutional layer applies a mathematical transformation to the output of the previous node, based on a certain piece of the image, denoted by the filter size. The convolved features are then pooled to provide parameters for the model. The model then selects features to use to classify images
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**In Progress** Creating a ConvNet to classify breast cancer histology slides while maintaining accountability through model interpretability methods
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