In this project, I classified images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset has been preprocessed, then a convolutional neural network has been trained on all the samples. I normalized the images, one-hot encoded the labels, built convolutional layers, max pool layers, and fully connected layers. At the end, you'll see their predictions on the sample images.
This project requires Python 3.x with the tensorflow library installed
To see the results open one of the following files:
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report.html
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image_classification.ipynb