Deep Learning with TensorFlow in Python

Classifying the letters with notMNIST dataset

Let’s first learn about simple data curation practices, and familiarize ourselves with some of the data that are going to be used for deep learning using tensorflow. The notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking a little more like real data: it’s a harder task, and the data is a lot less ‘clean’ than MNIST.

Preprocessing

First the dataset needs to be downloaded and extracted to a local machine. The data consists of characters rendered in a variety of fonts on a 28×28 image. The labels are limited to ‘A’ through ‘J’ (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine.

Let’s take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images downloaded.

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