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> Is there any way for those of us with average intelligence to contribute to tools like this? The people doing this aren't magical geniuses; they've just put
by magicalist 10y ago
> Is there any way for those of us with average intelligence to contribute to tools like this?
The people doing this aren't magical geniuses; they've just put the time and work into the subject and have been able to get themselves into a position they can do this all day surrounded by others they can collaborate with.
As with most human endeavors, the trick is to just get started, and not get frustrated and give up when it turns out you don't know anything at first. Some people don't mind starting with a ton of abstract learning about the subject, others prefer trying to accomplish specific tasks, learning the theory along the way.
For your specific question, as with all software, there's likely a lot to be done that has little to do with the main task of the tool and is just the everyday tasks of ease of use, interoperation with other tools, testing, etc. If on the other hand you want to get into the scary world of AI, like many others I'd recommend the Coursera machine learning course as a great place to start[1]
[1] https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning
- said 10y agoThank you for the link that course!
- bratsche 10y ago> The people doing this aren't magical geniuses Says the person named 'magicalist'. :)
- _audakel 10y agodouble upvote
- BOBOTWINSTON 10y agoI completed that course awhile back. Definitely a really great course if you can keep up. It is quite difficult, and I was quite terrible at it. I don't think I have any affinity for the topic, but at least now I can read/discuss the topic without being completely blind. There are also a lot of smaller components in the course that I found useful even not working directly in AI/ML. Just some general data modelling and linear algebra stuff was nice to pick up on the ride.
- visarga 10y agoSome AI professors recommend first jumping into a ML framework such as scikit-learn or Keras, which are more approachable, playing with them to the point where a little practical intuition will develop, and only then follow up with theory. Works better in practice than loading up on courses and math at the beginning, and helps the student form practical interest in the field and be more emotionally invested. In other words, first, hack your way in, and only then open the books.
- ChristianGeek 10y agoGood advice for life in general.
- achr2 10y agoI think this is the exact reason most people tend to struggle with any type of learning. Even topics like math and science which require building block approaches can benefit from taking a high level view and refining where appropriate.
- haraball 10y agoThe course on coursera uses Octave for the exercises. This guy has rewritten all the exercises in Python: http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ http://www.johnwittenauer.net/machine-learning-exercises-in-...