
Top-down learning path: Machine Learning for Software Engineers - zuzoovn
https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README.md
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vonnik
There are some valuable resources listed here.

The field of deep learning is moving really fast, and a lot of that research
is available on arXiv. Andrej Karpathy built a tool that surfaces interesting
papers: [http://www.arxiv-sanity.com/](http://www.arxiv-sanity.com/)

r/machinelearning tends to vote up significant work and news
[https://www.reddit.com/r/MachineLearning/](https://www.reddit.com/r/MachineLearning/)

And this list of papers may be helpful: [https://github.com/songrotek/Deep-
Learning-Papers-Reading-Ro...](https://github.com/songrotek/Deep-Learning-
Papers-Reading-Roadmap)

For total beginners, we created this page:
[https://deeplearning4j.org/deeplearningforbeginners](https://deeplearning4j.org/deeplearningforbeginners)

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misterbowfinger
My biggest challenge with CS papers is figuring out how to actually read them.
I've tried to read multiple CS papers and I still can't understand them. Am I
doing something wrong?

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tnecniv
1) Have a reasonable math background, or at least be willing to take it slow
and google.

2) Read the abstract, intro, and conclusion and see if the problem and result
are worth the effort.

3) Read the body. The whole body may or may not be relevant to you depending
on your interest. If you are starting out in a field, try to read and
understand the whole thing. You will learn lingo, what is expected of a
published paper in the field, improve your math skills, and make reading other
papers easier.

If you are having a lot of trouble with 1 or 3, find a MOOC relevant to the
subfield and go through it then go back to the literature.

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muninn_
Seems interesting. I haven't been able to look through all of the links, but I
assume there are practical exercises and "homework"?

When people say "I don't do or have the opportunity to do ML at work" it's
because they don't have data to analyze or actual things to program or do in
order to gain the experience they need to make the videos worthwhile to watch.

If I were to watch 10 videos on ML, but not actually go write code or analyze
data, then the videos aren't going to get me a ML job or be that helpful other
than learning a little about the topic.

It's kind of like watching a open course on ancient history or some similar
topic, but without writing a paper on it. Yes the video is interesting, but
what gets me a job and experience is the thought and work that goes into the
homework.

But even if these were just videos, it's a good resource.

~~~
zuzoovn
It has a lot of practical exercises, homework and dataset.

~~~
muninn_
Thanks!

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mysteryme
I appreciate a list of ML learning resources, but does anything about this
strike you as strange?

Over 9000 github stars and 1,260 forks for a repo started on 9th October????

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Eridrus
GitHub has no concept of bookmarks, so people star repos instead.

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visarga
It's great, but it's a mess - a heap of articles, github repos, books and
such. A new learner can't deal with that. What a new learner needs is an
interactive system to guide a learner through the process. Not a static list -
one size doesn't fit all. I might know Python and not know math, another one
might come from stats and Matlab but not know Python. One might know about
SVMs and Naive Bayes but not about neural nets, another might have played with
Keras neural nets but neglected to read up on basic ML. We need an adaptive
learning system. Who's going to make one?

~~~
zuzoovn
It's a step-by-step guide on how to learning machine learning from overview to
detail. Any comments are welcome.

If you want the interactive system, please check "A Visual Introduction to
Machine Learning:[http://www.r2d3.us/visual-intro-to-machine-learning-
part-1/"](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/")

~~~
lobut
I can easily infer your URL, but I think it includes an extra slash and
double-quote.

~~~
zuzoovn
Oops, this is my typo. Sorry about that.

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amelius
General ML question: is there some tool, supposedly based on ML, that can
learn from my editing behavior, and automatically deduce and apply editing
commands for repetitive editing tasks? This would be great e.g. when
refactoring code.

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notheguyouthink
This looks excellent, thank you for this! I actually get a chance to be
involved with some very very basic/light ML at work, but i'm also completely
new to the field. I really appreciate this style of guide, as i can hopefully
jumpstart my knowledge, while i work to gain the required math knowledge.

The Math will be my most difficult challenge, and i'm starting to brush up /
learn courses to hopefully enable the proper skillset. /fingerscrossed

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chrisweekly
For math try [https://betterexplained.com](https://betterexplained.com)

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notheguyouthink
Really appreciate that. I will be looking into that tonight, thank you!

My main source will likely be Khan, any sources you like more than Khan? Ie,
class room / instructor style _(class room is probably good, given the ease of
testing math)_

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sangaya
Looks to be both a thoughtful listing of resources / tutorials, but also
reinforces that to become ML capable, there is considerable effort involved.
Things worth doing may require a lot of work, and I'm happy this wasn't
another "Learn ML is 24 hours!" type resource.

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zuzoovn
Thanks, this is my multi-month study plan.

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devilsavocado
This is a great resource. I'd add Practical Deep Learning for Coders [0] to
the video series list.

[0] [http://course.fast.ai/index.html](http://course.fast.ai/index.html)

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zuzoovn
I added. Thanks

