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Managed to design and train (curve fit) custom CNN model with less than 7K animal images for 6 animal classifications without using data augmentation methods to acheive good curve fit. This was finally achieved by only using intel cpu laptop without GPUs that took 26 mins of epochs processing time. Python Keras library was used for this project.

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The Little CNN That Could: Small Machine Learning CNN with Small Dataset

Designed and developed CNN model to recognize challenging animal images by training small data set. I designed the CNN model by referring to following megaphone shapes to see which one actually predicted the correct animal image. Also used wireframe images to see if the predictions would improve. Found that the applying the final megaphone shape shown on the jupyter notebook had the greatest effect on prediction outcomes. Please refer to https://github.com/paul-data-science/Deep_Learning_CNN/blob/master/superhero_classifier_presentation_by_PaulAg.ipynb

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Managed to design and train (curve fit) custom CNN model with less than 7K animal images for 6 animal classifications without using data augmentation methods to acheive good curve fit. This was finally achieved by only using intel cpu laptop without GPUs that took 26 mins of epochs processing time. Python Keras library was used for this project.

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