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I'd definitely watch the first few episodes of Ng's stuff, up to and including logistic regression (unless you know all of that already, in which case: read pap

I'd definitely watch the first few episodes of Ng's stuff, up to and including logistic regression (unless you know all of that already, in which case: read papers and do practice projects for yourself--or compete in kaggle if you don't have any application ideas) The most common way to apply machine learning is supervised classification. The basic formula is: we learn a model (set of weights) to approximately map data (a matrix X) to corresponding labels (a matrix Y). Where you can use logistic regression to learn a set of weights, you can use a keras-based neural network. If all of that makes sense to you already, I think you're well prepared to read Keras' documentation.

krick 10y ago
It surely does make sense to me, but I seriously think (maybe hope, even?) that "hacking-driven" approach here is significantly overvalued. Because of sociological reasons. After all, all this is mathematical problems, and while I'm aware that NNs are pretty much unexplored space, there surely must exist some quite significant amount of knowledge at level below the NNs that can be actually systematically learned. All these various statistical methods R-lang community is buzzing about which I'm not ever aware of, some rationale about "why NN and not just a regression", etc. You know, the math.
- dave_sullivan 10y ago
  If you just pick up a math book, you'll learn lots of stuff that you don't need to know. That's fine, but it strikes me as a good way to avoid actually doing anything and gaining practical experience. If you hit a wall in practice because you don't understand the math, you'll usually have enough of an idea of the problem to ask more intelligent questions about what kind of math you need. That will, incidentally, help you understand the math better because you're coming to it out of an actual need rather than just seeing it mixed into a bunch of chapters. Unless you're going to write a machine learning framework or be a researcher, the required math isn't too bad and it sounds like you might have enough of a background already. So don't be afraid to dive into something practical (like a kaggle competition). FWIW this is a really good blog for insight into the math and intuition behind deep learning: http://colah.github.io/ http://colah.github.io/ (i'm not sure if it's quite what you're looking for though)
- nl 10y ago
  The problem with looking for a theoretical as to why one method should be chosen over another is that you run into the "No Free Lunch theorem"[1]: any two optimization algorithms are equivalent when their performance is averaged across all possible problems Once you accept that, then you start looking at practical considerations. Having said that, if you do want to do the math then you might like the course from Oxford/Nando DeFreitas (now at DeepMind/Oxford)[2] [1] https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem [2] https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPo..., https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...

krick 10y ago

It surely does make sense to me, but I seriously think (maybe hope, even?) that "hacking-driven" approach here is significantly overvalued. Because of sociological reasons. After all, all this is mathematical problems, and while I'm aware that NNs are pretty much unexplored space, there surely must exist some quite significant amount of knowledge at level below the NNs that can be actually systematically learned. All these various statistical methods R-lang community is buzzing about which I'm not ever aware of, some rationale about "why NN and not just a regression", etc. You know, the math.

dave_sullivan 10y ago
If you just pick up a math book, you'll learn lots of stuff that you don't need to know. That's fine, but it strikes me as a good way to avoid actually doing anything and gaining practical experience. If you hit a wall in practice because you don't understand the math, you'll usually have enough of an idea of the problem to ask more intelligent questions about what kind of math you need. That will, incidentally, help you understand the math better because you're coming to it out of an actual need rather than just seeing it mixed into a bunch of chapters. Unless you're going to write a machine learning framework or be a researcher, the required math isn't too bad and it sounds like you might have enough of a background already. So don't be afraid to dive into something practical (like a kaggle competition). FWIW this is a really good blog for insight into the math and intuition behind deep learning: http://colah.github.io/ http://colah.github.io/ (i'm not sure if it's quite what you're looking for though)
nl 10y ago
The problem with looking for a theoretical as to why one method should be chosen over another is that you run into the "No Free Lunch theorem"[1]: any two optimization algorithms are equivalent when their performance is averaged across all possible problems Once you accept that, then you start looking at practical considerations. Having said that, if you do want to do the math then you might like the course from Oxford/Nando DeFreitas (now at DeepMind/Oxford)[2] [1] https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem [2] https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPo..., https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...