Welcome to the PyTorch tutorials for the Duke University +Data Science program! These materials are meant to accompany the +Data Science In-Person Learning Experiences and AI for Everyone course, as well as the Duke Introduction to Machine Learning Coursera. Completion of the coding exercises provided here is not required, but we highly encourage it! Many of the machine learning concepts being covered thoughout these programs are best learned and reinforced by implementing the ideas in code yourself.
While we aim to present these materials in an accessible manner, we do assume a certain background in scientific computating, specifically some familiarity with Python and NumPy. If you haven't used Python before, or want a refresher, we recommend Python Like You Mean It, by Ryan Soklaski. This free e-book consists of five short modules introducing Python for scientific computing and data analysis. Alternatively, a quick run-through of relevant concepts is provided in 0A_Python_Prerequisites.ipynb
If you'd like to run these examples on your own machine, we've provided installation instructions in 1A_PyTorch_Installation.ipynb. We'll be spending most of our time coding in IPython notebooks; if you haven't used an IPython notebook before, 1B_Coding_Environments.ipynb will give you a quick primer, as well as a few alternatives.