Description
This talk focuses on the code and concepts behind image classification models. First, I'll break down how convolutional neural networks work. I'll present a few methods for dealing with small amounts of training data (data pre-processing and image augmentation / transformations). I'll show how to leverage (and extend) a network that's been pre-trained on a large image dataset (transfer learning). I'll use libraries like Keras and Tensorflow, and I'll show a working prototype (and open-source it) that identifies photos of pizza, because pizza (NY style of course) is way better than hotdogs :)