We can't discuss Convolutional neural networks before skimming over convolution and pooling theory first. Both are simpler than you think, but extremely capable in image classification. Convolutional neural networks are a special type of neural network used for image classification. At the heart of any convolutional neural network lies convolution, an operation highly specialized at detecting patterns in images. Convolutional layers require you to specify the number of filters.

It should be big enough to train a decent image classifier, but not with ANNs. The only problem is -- it's not structured properly for deep learning out of the box. Let's get the library imports out of the way. We'll need quite a few of them, so make sure to have Numpy, Pandas, TensorFlow, PIL, and Scikit-Learn installed: You can't pass an image directly to a Dense layer. A single image is 3-dimensional -- height, width, color channels -- and a Dense layer expects a 1-dimensional input.