Timeline for What are the Wolfram Language's relative strengths for machine learning?
Current License: CC BY-SA 4.0
22 events
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| Mar 26, 2020 at 5:26 | history | edited | Victor K. | CC BY-SA 4.0 |
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| Jan 12, 2019 at 17:00 | comment | added | Anton Antonov | @C.E. "I think it would be best to mention it in your answer, go ahead." -- Done. (I deleted my question to you about that here...) | |
| Jan 12, 2019 at 16:46 | comment | added | C. E.♦ | @gwr I don't know about that, when I graduated I didn't have much that distinguished me except for my involvement in the Mathematica community. It has been very useful for me. But I agree to the extent that companies will not ask you to do programming in Mathematica, you will have to explain to them why your skill in Mathematica is worth considering in terms of problem solving skills, paradigms, knowledge sharing that you've done etc. | |
| Jan 11, 2019 at 18:33 | comment | added | gwr | @C.E.Certainly my comment was intended to provoke, but there is truth in the fact that in a market the technologically best solution (if that were identifyable) might not be the winner. Open source availability and breadth of user base certainly play into the hands of R and Python imho. Looking for a job? Don’t bother to mention WL for example... or not? | |
| Jan 11, 2019 at 18:25 | comment | added | C. E.♦ | @gwr As I understand it, Kaggle kernels can only be written in R or Python. That's why you won't see anything else. This is a choice that managers at Kaggle have made, it doesn't mean that R and Python are the best languages for machine learning. | |
| Jan 11, 2019 at 15:54 | comment | added | Anton Antonov | @gwr See my answer/rant. | |
| Jan 11, 2019 at 15:22 | answer | added | Anton Antonov | timeline score: 10 | |
| Jan 11, 2019 at 8:30 | history | edited | gwr |
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| Jan 11, 2019 at 8:22 | comment | added | gwr | Somebody correct me, but as of now I only see R or Python Kernels for Kaggle (maybe Julia?). It seems that Python is the quasi standard for any serious ML. | |
| Jul 22, 2018 at 13:19 | comment | added | Anton Antonov | I think it is a good idea to give answers to these questions in the original post. 1. Why should we care about Kaggle competitions? 2. What type of machine learning problems are in Kaggle (supervised, unsupervised, etc.) 3. What type of usability is considered? Meaning, out of the box functionalities, and/or the ability to program novel algorithms. | |
| Jul 21, 2018 at 16:44 | answer | added | hobs | timeline score: 10 | |
| May 4, 2016 at 9:22 | answer | added | ArtificialBrilliance | timeline score: 17 | |
| Jun 29, 2015 at 6:57 | history | tweeted | twitter.com/#!/StackMma/status/615413872892248065 | ||
| Jun 28, 2015 at 3:00 | answer | added | Mark Kotanchek | timeline score: 19 | |
| Jun 24, 2015 at 6:52 | comment | added | Gordon Coale | Its worth noting that a good proportion of the competitions on kaggle require Open source solutions thus ruling out MMA. | |
| Jun 23, 2015 at 21:14 | answer | added | dr.blochwave | timeline score: 14 | |
| Jun 23, 2015 at 20:59 | answer | added | David G. Stork | timeline score: 65 | |
| Jun 23, 2015 at 19:35 | answer | added | Gordon Coale | timeline score: 15 | |
| Jun 23, 2015 at 18:40 | comment | added | Leonid Shifrin | To the closers: while I partly agree that the question is a bit broad, it seems to be an important one, and having some good answers for it is important. I also think that the decision of being too broad or not should depend on how much specialized information on a praticular topic is currently available on the site. For ML, there isn't yet much accumulated common wisdom on this site in the context of Mathematica. When we started the site, we had similarly broad questions on other topics (like, e.g., meta-programming), which seem to have proved highly appreciated despite that. | |
| Jun 23, 2015 at 18:00 | review | Close votes | |||
| Jun 23, 2015 at 19:10 | |||||
| Jun 23, 2015 at 17:50 | answer | added | image_doctor | timeline score: 49 | |
| Jun 23, 2015 at 15:50 | history | asked | andandandand | CC BY-SA 3.0 |