Hi Vladn, Here is the updated proposal, I have added the current challenges and proposed solutions on the abstract,
https://google-melange.appspot.com/gsoc/proposal/review/google/gsoc2013/issamou/1# Thank you! On 5/2/2013 11:34 AM, Vlad Niculae wrote: > Sorry, I just saw that your submission is on Melange. > > I think the proposal could use some discussion on what issues might be > faced. Many people here have expressed concerns about including "deep > stuff", the difficulty to have sensible defaults, the difficulty to > having a general-purpose efficient implementation that can be used on > different domains without hacking the code. In the very simple RBM, > the example is still unsatisfactory because it is hard to show off the > algorithm on too small a dataset. This might be even trickier with > deeper things. > > In tuning a good neural model some know-how and tricks are needed, > many times you need to look over the training process and measure > statistics. It would be useful to describe this kind of difficulties > and how we might be able to avoid them, what kind of hyperparameter > heuristics / initialization should be used, etc. It is early to go > into it too deeply (pun intended) but I think the proposal can benefit > by your embracing the skeptic side. > > Hope this helps, > Vlad > > > On Thu, May 2, 2013 at 5:20 PM, Vlad Niculae <zephy...@gmail.com> wrote: >> Hi Issam, >> >> The deadline is fast approaching. How is your proposal going? Could >> you share a version so we can give some feedback? >> >> Yours, >> Vlad >> >> On Sat, Apr 20, 2013 at 3:57 AM, amir rahimi <noname01....@gmail.com> wrote: >>> Sorry, I didn't see Andy's note ;) >>> >>> >>> On Fri, Apr 19, 2013 at 11:23 PM, amir rahimi <noname01....@gmail.com> >>> wrote: >>>> Hi, >>>> I recommend Theano if you want to use python with GPU for deep learning. >>>> It is tightly integrated with numpy.... >>>> >>>> Best, >>>> Amir >>>> >>>> >>>> On Thu, Apr 18, 2013 at 9:21 PM, Wei LI <kuant...@gmail.com> wrote: >>>>> @Andy What do you mean by "blackbox" algorithm? Does that mean something >>>>> similar to pylearn2? >>>>> >>>>> @Issam, It seems to me that scalablity is a key factor to train deep >>>>> models and make them work. Do you have any suggestion how to make it >>>>> scalable while still fits in sklearn framework? I think sklearn cannot >>>>> supports GPU easily. I wanna know is training a deep model for a mid-level >>>>> scale(maybe like cifar?) painful on CPU only with numpy? >>>>> >>>>> Best, >>>>> Wei >>>>> >>>>> On Fri, Apr 19, 2013 at 12:27 AM, Andreas Mueller >>>>> <amuel...@ais.uni-bonn.de> wrote: >>>>>> Hi Issam. >>>>>> Thank you for your interest. Have you looked at the >>>>>> MLP and RBM pull requests that are currently open? >>>>>> How would your project relate to those? >>>>>> >>>>>> A real problem is that we don't want to replicate theano >>>>>> and rather have a somewhat "black box" algorithm that people can >>>>>> apply.... >>>>>> >>>>>> Cheers, >>>>>> Andy >>>>>> >>>>>> >>>>>> On 04/18/2013 06:07 PM, Issam wrote: >>>>>>> Hi scikit, >>>>>>> >>>>>>> Here I am proposing to work on deep learning topic for GSOC 2013. Deep >>>>>>> learning is a relatively new research area that is progressing fast >>>>>>> with a lot of potential for contributions. It involves an intersting >>>>>>> idea by trying to imitate the brain, as it uses many levels (hidden >>>>>>> layers) of processing. Where the levels are at decreasing order of >>>>>>> abstractions! >>>>>>> >>>>>>> In this project, I'm planning to work on each step carefully, first I >>>>>>> look into "Deep Boltzmann machines", then "Deep belief >>>>>>> networks","Deep >>>>>>> auto-encoders", "Stacked denoising auto-encoders", and more. I could >>>>>>> create a complete plan for this, once I get your feedback :) >>>>>>> >>>>>>> I have been involved in quite a number of machine learning projects, >>>>>>> from dealing with imbalanced datasets (software quality prediction), >>>>>>> to >>>>>>> XML classification, from recognizing gender out of handwriting, to >>>>>>> breast cancer prediction using mammograms. I'm in my second semester >>>>>>> as >>>>>>> a graduate student (MSc), and machine learning is my research area. My >>>>>>> thesis would involve deep learning, which i will apply on >>>>>>> bioinformatics >>>>>>> and face recognition. >>>>>>> >>>>>>> I would be more than happy to work with a mentor on this! >>>>>>> >>>>>>> Thank you! >>>>>>> >>>>>>> Best regards, >>>>>>> --Issam Laradji >>>>>>> >>>>>>> >>>>>>> ------------------------------------------------------------------------------ >>>>>>> Precog is a next-generation analytics platform capable of advanced >>>>>>> analytics on semi-structured data. The platform includes APIs for >>>>>>> building >>>>>>> apps and a phenomenal toolset for data science. Developers can use >>>>>>> our toolset for easy data analysis & visualization. Get a free >>>>>>> account! >>>>>>> http://www2.precog.com/precogplatform/slashdotnewsletter >>>>>>> _______________________________________________ >>>>>>> Scikit-learn-general mailing list >>>>>>> Scikit-learn-general@lists.sourceforge.net >>>>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>>>> >>>>>> >>>>>> ------------------------------------------------------------------------------ >>>>>> Precog is a next-generation analytics platform capable of advanced >>>>>> analytics on semi-structured data. The platform includes APIs for >>>>>> building >>>>>> apps and a phenomenal toolset for data science. Developers can use >>>>>> our toolset for easy data analysis & visualization. Get a free account! >>>>>> http://www2.precog.com/precogplatform/slashdotnewsletter >>>>>> _______________________________________________ >>>>>> Scikit-learn-general mailing list >>>>>> Scikit-learn-general@lists.sourceforge.net >>>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>>> >>>>> >>>>> >>>>> -- >>>>> LI, Wei >>>>> Tsinghua/CUHK >>>>> http://kuantkid.github.com/ >>>>> >>>>> >>>>> >>>>> ------------------------------------------------------------------------------ >>>>> Precog is a next-generation analytics platform capable of advanced >>>>> analytics on semi-structured data. The platform includes APIs for >>>>> building >>>>> apps and a phenomenal toolset for data science. Developers can use >>>>> our toolset for easy data analysis & visualization. Get a free account! >>>>> http://www2.precog.com/precogplatform/slashdotnewsletter >>>>> _______________________________________________ >>>>> Scikit-learn-general mailing list >>>>> Scikit-learn-general@lists.sourceforge.net >>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>>> >>>> >>>> >>>> -- >>>> ---------------------------------------------------------------------- >>>> #include <stdio.h> >>>> double d[]={9299037773.178347,2226415.983937417,307.0}; >>>> main(){d[2]--?d[0]*=4,d[1]*=5,main():printf((char*)d);} >>>> ---------------------------------------------------------------------- >>> >>> >>> >>> -- >>> ---------------------------------------------------------------------- >>> #include <stdio.h> >>> double d[]={9299037773.178347,2226415.983937417,307.0}; >>> main(){d[2]--?d[0]*=4,d[1]*=5,main():printf((char*)d);} >>> ---------------------------------------------------------------------- >>> >>> ------------------------------------------------------------------------------ >>> Precog is a next-generation analytics platform capable of advanced >>> analytics on semi-structured data. The platform includes APIs for building >>> apps and a phenomenal toolset for data science. Developers can use >>> our toolset for easy data analysis & visualization. Get a free account! >>> http://www2.precog.com/precogplatform/slashdotnewsletter >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> > ------------------------------------------------------------------------------ Introducing AppDynamics Lite, a free troubleshooting tool for Java/.NET Get 100% visibility into your production application - at no cost. Code-level diagnostics for performance bottlenecks with <2% overhead Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap1 _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
