Introduction to PyTorch

Recently, Microsoft and PyTorch announced a "PyTorch Fundamentals" tutorial, which you can find on Microsoft's site and on PyTorch's site. The code in this post is based on the code appearing in that tutorial, and forms the foundation for a series of other posts, where I'll explore other machine learning frameworks and show integration with Azure ML. In this post, I'll explain how you can create a basic neural network in PyTorch, using the Fashion MNIST dataset as a data source. The neural network we'll build takes as input images of clothing, and classifies them according to their contents, such as "Shirt," "Coat," or "Dress." I'll assume that you have a basic conceptual understanding of neural networks, and that you're comfortable with Python, but I assume no knowledge of PyTorch. Let's start by getting familiar with the data we'll be using, the Fashion MNIST dataset. This dataset contains 70,000 grayscale images of articles of clothing -- 60,000 meant to be used for training and 10,000 meant for testing.