Welcome to the Duke Machine Learning Summer School 2019! This repository will contain the lecture materials and assignments for the hands-on TensorFlow sessions.
While there is no hard requirement to attend these sessions or complete the exercises, we do strongly recommend them! Many of the machine learning concepts being covered thoughout the week are best learned and reinforced by implementing the ideas in code yourself. Please come ready to code!
Please follow the instructions in the notebook labeled 00A_TensorFlow_Installation.ipynb. It will guide you through the installation of Python, TensorFlow, and Jupyter Notebook, the software we'll be using for our afternoon "hands-on" sessions.
Given the pace of the course, we'll be assuming some background knowledge for scientific computing in Python. If you are unfamiliar with IPython notebooks or Python coding environments, a brief introduction can be found in 00B_Coding_Environments.ipynb.
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. Modules 2 and 3 are especially relevant: Module 2 covers Python essentials, and Module 3 covers the manipulation of matrices and vectors in Python.
Additionally, we'll be releasing new lecture materials to this GitHub repository each day of the course. If you're familiar with Git, the most seamless way to keep your files up-to-date is by cloning this repository and pulling. Otherwise, you can click the green "Clone or Download" button at the top of this page, and select "Download as ZIP" to download a copy of all these files.