Project Description

In today's world, almost on a daily basis Joe Average is confronted with technology that is supposedly using some form of AI, ML, DL.

I've grown up merely with "algorithms and data structures" later supplemented by "class objects and methods" etc.

And basically in my world view computers perform just the "stupid" tasks defined/determined/restricted by hardware/software and certain input values.

Thus for me the question is where's the boundary that defines AI and where does learning comes into play?

To get this sorted out I've come up with some heretic questions:

  • Are AI, ML, DL just buzzwords?
  • What's the definition and distinction of those three?
  • What's specific to algorithms used for it?
  • Where is it used?
  • What (open-source) software does exist and is (widely) used and where?
  • ...

Goal for this Hackweek

My goal is to get above questions answered and to gain a better understanding of AI, ML, DL.

Hopefully in the end I know what's specific to it, where it is used, and what software does exist in this area.


Looking for hackers with the skills:

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This project is part of:

Hack Week 20


  • 4 months ago: stefannica liked this project.
  • 4 months ago: awh originated this project.

  • Comments

    • awh
      4 months ago by awh | Reply

      From what I've read so far I've found some entries in quite interesting, e.g. (AI - the no bullshit approach) [Posted 10 months ago by Filip Piekniewski]

    • awh
      4 months ago by awh | Reply

      Perhaps of interest:

      • awh
        4 months ago by awh | Reply

        So far for the crash-course I just had to ensure installation of numpy, pandas, tensorflow2.

      • awh
        4 months ago by awh | Reply

        The linear regression exercises at were quite illustrative.

      • awh
        4 months ago by awh | Reply

        What makes this course also important is that terminology specific to ML is explained, e.g. one-hot-encoding, feature cross etc.

    • awh
      4 months ago by awh | Reply

      WRT papers. There are quite some listed in github, e.g.

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