In this project, you'll classify images from the CIFAR-10 dataset
. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.
- Ensure you've passed all the unit tests in the notebook (Completed)
- Ensure you pass all points on the rubric (Completed)
- Save File as
dlnd_image_classification.ipynb
(Completed) - Save File as
dlnd_image_classification.html
(Completed) - Include helper.py and problem_unittests.py files (Completed)