Welcome to part three of Deep Learning with Neural Networks and TensorFlow, and part 45 of the Machine Learning tutorial series. In this tutorial, we're going to be heading (falling) down the rabbit hole by creating our own Deep Neural Network with TensorFlow.

We're going to be working first with the MNIST dataset, which is a dataset that contains 60,000 training samples and 10,000 testing samples of hand-written and labeled digits, 0 through 9, so ten total "classes." I will note that this is a very small dataset in terms of what you would be working with in any realistic setting, but it should also be small enough to work on everyone's computers.

The MNIST dataset has the images, which we'll be working with as purely black and white, thresholded, images, of size 28 x 28, or 784 pixels total. Our features will be the pixel values for each pixel, thresholded. Either the pixel is "blank" (nothing there, a 0), or there is something there (1). Those are our features. We're going to attempt to just use this extremely rudimentary data, and predict the number we're looking at (a 0,1,2,3,4,5,6,7,8, or 9). We're hoping that our neural network will somehow create an inner-model of the relationships between pixels, and be able to look at new examples of digits and predict them to a high degree.

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