Deep Learning: Doubly Easy and Doubly Powerful with GraphLab Create by Piotr Teterwak.
From the post:
One of machine learning’s core goals is classification of input data. This is the task of taking novel data and assigning it to one of a pre-determined number of labels, based on what the classifier learns from a training set. For instance, a classifier could take an image and predict whether it is a cat or a dog.
The pieces of information fed to a classifier for each data point are called features, and the category they belong to is a ‘target’ or ‘label’. Typically, the classifier is given data points with both features and labels, so that it can learn the correspondence between the two. Later, the classifier is queried with a data point and the classifier tries to predict what category it belongs to. A large group of these query data-points constitute a prediction-set, and the classifier is usually evaluated on its accuracy, or how many prediction queries it gets correct.
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Despite a slow start, the post moves onto deep learning and GraphLab Create in detail, with code. You will need the GPU version of GraphLab Create to get the full benefit of this post.
Beyond distinguishing dogs and cats, a concern for other dogs and cats I’m sure, what images would you classify with deep learning?
I first saw this in a tweet by Aapo Kyrola