Hello Jacob,
Thanks a lot for your suggestions! I updated my proposal
<https://gist.github.com/kkatrio/9710b0a37f042a2df784cda7082d5523>. I
will add some minor details later today.
Regarding the codebase, I am thinking about editing
linear_model/sgd_fast.pyx for softmax and adding a new
linear_model/sgd_opt.pyx perhaps for AdaGrad and Adam, I don't know if
you agree on that.
I admit I am a total beginner in Cython but I have time until June to
practice.
If there is real interest in the project and time to mentor my proposal
please let me know. Ideally I would prefer not to leave it till the last
day.
Kind regards,
Konstantinos
On 30/03/2017 07:45 πμ, Jacob Schreiber wrote:
Hi Konstantinos
I likely won't be a mentor for the linear models project, but I looked
over your proposal and have a few suggestions. In general it was a
good write up!
1. You should include some equations in the write up, basically the
softmax loss (which I think is a more common term than multinomial
logistic loss) and the AdaGrad update.
2. You may want to indicate which files in the codebase you'll be
modifying, or if you'll be adding a new file. That will show us you're
familiar with our existing code.
3. You should give more time for the cython implementation of these
methods. It's not that easy to do, especially if you don't have
background experience. You can easily lose a day or two from a dumb
memory error that has nothing to do with if you understand the equations.
4. You might want to also implement ADAM if time permits. It's another
optimizer that is popular. I'm not sure how popular it is in linear
models but I've seen it used effectively, and once you get AdaGrad it
should be easier to implement a second optimizer.
Good luck!
Jacob
On Mon, Mar 27, 2017 at 10:43 AM, Konstantinos Katrioplas
<konst.katriop...@gmail.com <mailto:konst.katriop...@gmail.com>> wrote:
Dear all,
here is a draft of my proposal
<https://gist.github.com/kkatrio/9710b0a37f042a2df784cda7082d5523>on
improving online learning for linear models with softmax and AdaGrad.
I look forward to your feedback,
Konstantinos
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