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Class 10

😱 Final projects

Goals:

  • Combine your interests (code and not-code)
  • Build something meaningful for yourself
  • Experiment with a specific technology that interests you
  • Flex your skills
  • Learn some new techniques & revisit topics we've discussed in class

Deliverables:

  • The code itself, uploaded to Canvas or hosted on your own website
  • One or more of the following:
    • A live version of your code running on a website
    • A .zip file with the code that can be run locally
    • A video recap of the code in action
    • A live performance??
  • Documentation:
    • Screenshots and/or video screen recordings that show off the output and program features
    • A description of the piece (and title if applicable). At least 1 paragraph about what you've created, what you learned, tools, techniques, and anything else that makes your project interesting. Tell a story.
    • Documentation should ideally go into your portfolio or exist on the web somewhere. You host it on your own website, the p5js editor, YouTube/Vimeo, or otherwise.

🛠️ Machine Learning & AI

🎤 Case study: Me+You - voice recognition, volumetric shapes, and 3D simulation in a real ML-driven install

How to do ML/AI things

  1. (Beginner) Use a friendly, commercially-available ML tool
  2. (Intermediate) Find or download a tool/library/service that someone else has created, and use it
  3. (Advanced) Download a tool that someone else has made, and train your own data set, then use that
  4. (Expert) Write your own ML tool using existing ML frameworks like PyTorch or Tensorflow

* Just using a ML tool that is ready to use can be an expert-level journey

How to install an ML tool:

  • Some ML tools are fully hosted online and you don't need to install anything
  • Some will be pre-packaged into a nice UI in a desktop app. This is rare
  • Some have online notebooks that are ready to use, usually in Google Colab. This creates a virtual environment with a good GPU to use. You should be able to copy a notebook into your own acount and start using it. This will also usually cost money if you want a very good GPU.
  • Some must be installed on your own computer. Sometimes a very nice GPU is required (look for GPU RAM requirements). Often Linux and Windows are the target platforms
  • Many existing apps are now adding AI features

Find an ML tool

ML is really good at dealing with images, so many of the popular machine-learning tools are either image generators or analyzers. However, some other popular ML tools deal with text, audio, and other data types to do amazing, magical things. Some places to start, depending on your interests:

Articles / videos

See Learning With AI

Courses / Tutorials

Interesting ML Tools

For a living, up-to-date list (this section ages fast - check here first): creative-coding-notes#machine-learning

Beginner-friendly

Computer vision

More advanced

🔍 Further (older/historical tools - still interesting, less likely to come up live)

ML/AI Artists

Diversity in ML

ML: The Good Things

ML: The Bad Things

Biases in Software

Not necessarily specific to ML, but exacerbated by ML. Who is building the software that we use?

📝 Homework:

Read:

  • Some of the articles above
  • Investigate some ML tools

Build something with a machine-learning tool

  • Or start work on your final project and present that next week

📋 Review code

  • Present your 3D/Computer Vision sketches