Material from previous years. Preserved for archival purpose. The links may be outdated and not working.
Read the Introduction to Coding slides
Sign up to Google Colab and clone Lesson 1 (if you didn't already)
Work on Lesson 1 (In order to work on the lesson and run the code you need to save your own copy, the link is to a demo copy of the lesson)
Work on Lesson 1 Challenges (In order to work on the notebook and run the code you need to save your own copy, the link is to a demo copy)
To go back and continue to work on notebooks you already started, log-in to Google Colaboratory and click on the desired notebook.
Here is an example of Solutions to Lesson 1 Challenges
Complete section 1 to 4 in the Git Introduction (follow all the links): Learn about Git and GitHub, fork the Lesson 2 repository and import it in Google Colaboratory
Work on Lesson 2 : log-in to Google Colaboratory and click on Lesson2 (target_python_lesson2.ipynb)
Work on Lesson 2 Challenges: log-in to Google Colaboratory and click on the Lesson2 challenges (target_python_challenge2.ipynb). The Lesson 2 folder includes also the NOVA file transfer data provided by Andrew Norman
To go back and continue to work on notebooks you already started, log-in to Google Colaboratory and click on the desired notebook
(optional) Complete the tutorial linked in section 5 of the Git Introduction to learn to use git to manage files on your computer (using the command line)
Here is an example of Solutions to Lesson 2 Challenges
Fork the Lesson 3 repository and import it in Azure Notebooks (see Lesson 2, step 2 if you don't remember how)
Work on Lesson 3 : log-in to Google Colaboratory and click on the Lesson3 (target_python_lesson3.ipynb)
Work on Lesson 3 Challenges: log-in to Google Colaboratory and click on the Lesson3 challenges (target_python_challenge3.ipynb)
To go back and continue to work on notebooks you already started, log-in to Google Colaboratory and click on the desired notebook.
Here is an example of Solutions to Lesson 3 Challenges
This material can be completed out of order. It is recommended to have completed at least Lesson 2.
Clone and complete the Mathematical plots examples (play extensively with the content, changing the functions to plot)
Clone and complete A Neural Algorithm of Artistic Style, by Gabriel Purdue. This Jupyter Notebook is on Google Colab(oratory), another free notebooks hosting.
To start, login with a Google account (or create a new one), make a copy on your drive (menu "File" > "Save a copy in Drive...") and open your copy with Colab. Alternatively you can click "OPEN IN PLAYGROUND" (this will not save your changes but will allow you to run the code).
Before running the code, open the menu "Runtime" > "Change runtime type" and make sure that your Runtime type is Python 3 and the Hardware accelerator is GPU (Colab allows you to use GPUs!).
Follow the tutorial: read about Neural Networks, play around with the TensorFlow Playground, read about the algorithm for artistic style and transform the Fermilab picture
Now use choose an image you like and apply the "Van Gogh" style to it
In this blog post you can find a demo similar to the distributed spot counting demo presented by Ken Herner. You can run the code locally. To run on remote clusters it requires access to Open Science Grid resources.
The 2019 class schedule is also on Fermilab's Indico (first lesson)
2023 Outline coming soon!