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Top-down learning path: Machine Learning for Software Engineers

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Re: Top-down learning path: Machine Learning for Software Engineers

#21

Post by 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/

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

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

For total beginners, we created this page: https://deeplearning4j.org/deeplearningforbeginners

Re: Top-down learning path: Machine Learning for Software Engineers

#22

Post by mindcrime »

muninn_ wrote: post #3

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 w…

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.

One thought about that: If you don't have "work data" to work on, there's still TONS of other "stuff" out there that you can work with. And given how much Open Data / Linked Data datasets are out there, for many industries I'd bet there's a pretty good chance one could find some interesting analysis of one of those datasets that would have value for their employer. Digging in and building something like that could help with bridging into an ML role. Or, if it's obvious that it just Isn't Going To Happen with one's current employer, it's always possible to just build something cool around an Open Data dataset and plop it up on GitHub to show other people. And in either case, it provides the motivation of having a project to work on.

There are also Kaggle competitions.

Re: Top-down learning path: Machine Learning for Software Engineers

#23

Post by zuzoovn »

lobut wrote: post #14
zuzoovn wrote: post #10

Earlier quoted context omitted.

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/"

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

Oops, this is my typo. Sorry about that.

Re: Top-down learning path: Machine Learning for Software Engineers

#25

Post by zuzoovn »

sangaya wrote: post #2

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.

Thanks, this is my multi-month study plan.

Re: Top-down learning path: Machine Learning for Software Engineers

#26

Post by misterbowfinger »

vonnik wrote: post #21

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/ r/machinelearning tends to vote up significant work and news https://www.reddit.com/r/MachineLearning/ And this list of papers may be helpful: https://github.com/songrotek/Deep-Lear…

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?

Re: Top-down learning path: Machine Learning for Software Engineers

#27

Post by mooneater »

misterbowfinger wrote: post #26
vonnik wrote: post #21

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/ r/machinelearning tends to vote up significant work and news https://www.reddit.com/r/MachineLearning/ And this list of papers may be helpful: https://github.com/songrotek/Deep-Lear…

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?

Read the abstract and confirm it is relevant to you. In general, you will need to read papers if you want to be on the bleeding edge of the latest in research. That is machine learning research, and that is very specialized. But you do not necessarily need to be on the bleeding edge to be an effective machine learning engineer. For that you can use often less abstract and more digestible sources, including MOOC courses, tutorials, books of multiple levels, vendor-specific docs, etc.

Re: Top-down learning path: Machine Learning for Software Engineers

#28

Post by tnecniv »

misterbowfinger wrote: post #26
vonnik wrote: post #21

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/ r/machinelearning tends to vote up significant work and news https://www.reddit.com/r/MachineLearning/ And this list of papers may be helpful: https://github.com/songrotek/Deep-Lear…

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?

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.

Re: Top-down learning path: Machine Learning for Software Engineers

#29

Post by chrisweekly »

notheguyouthink wrote: post #7

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 prop…

For math try https://betterexplained.com

Re: Top-down learning path: Machine Learning for Software Engineers

#30

Post by notheguyouthink »

chrisweekly wrote: post #29
notheguyouthink wrote: post #7

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 prop…

For math try https://betterexplained.com

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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