2022-05-09 Meeting notes

Discussion about materials collected so far

• Ben
  • Shared some details about ESIP funding opportunity - https://www.esipfed.org/lab/funding-opportunities/ml-rfp - also shared in Slack - later the group discussed about some potential ways forward including exploring existing projects that may be interested.
•  Vismayak
  • Attended NVIDIA workshop about using ML frameworks on GPUs 
  • https://rapids.ai/about.html
• Kastan
  • Explored https://gradio.app/ - a fast way to demo machine learning model
  • https://huggingface.co/models
  • Shared news about AI hackathon - Congratulations!!
  • https://automl.github.io/auto-sklearn/master/ could be an option for beginners  
  • https://kastanday.notion.site/kastanday/A-Theoretical-Analysis-of-the-Algorithms-that-Win-Kaggle-Competitions-aef85fe8fb4943909da00e6c24e2142a#63db55b4fabb4c9ba920241fb8d9a4fb
• Sandeep
  • Explored
    • https://machinelearningmastery.com/start-here/

      • Doesn't provide a specific pathway, but seems to include a broad range of topics including foundation, beginner, intermediate and advanced.

      • This website is popular amongst the ML learners community.

      • Some content is old dating back to 2016 - may be those are mainly philosophical in nature (e.g. motivational material)

    • https://i.am.ai/roadmap/#machine-learning-roadmap

      • Interactive

      • Some are links to Wikipedia pages

    • https://www.mltut.com/deep-learning-roadmap/

      • Specifically deep learning focused

      • A sub-topic is about general ML

      • Links to online courses

    • https://www.mltut.com/machine-learning/

      • Very short definitions

      • Some links to online courses

• Documentation

• Discussed starting to write the final [DRAFT] Hands-on Machine Learning Study Materials for Research Software Engineers - Focus Group Report with different sections assigned to different members - Thanks Minu for bringing this up.
• The goal of this document is to write short bullet points/headings about different sub-topics and link a few relevant/good study materials (including hands-on materials) to those topics. Whenever possible, please add some short comments about the linked material (e.g., good for beginners, mostly hands-on activities, good theoretical explanations, etc.)
• The goal of this document is NOT to provide detailed descriptions for each sub-topics.
• Assigned a few section to start with.
• Others, please find relevant areas from the goals listed at Hands-on Machine Learning Study Materials for Research Software Engineers and add those topics and assign to yourself.
• https://paperswithcode.com/sota - useful to provide pointers to the state of the art in different areas - rather than individual models.