Hi Marcus, I'hv also created a wiki page on github. https://github.com/abhinvgpta/mlpack/wiki/Example-of-Feed-Forward-Network-using-mlpack Please have a look.
Thanks, Abhinav On Mon, Apr 18, 2016 at 1:16 PM, Abhinav Gupta <[email protected]> wrote: > Hi Marcus, > I'hv preprocessed the Internet Advertisements dataset and implemented > the example using FFN. I'hv created and shared a google doc with you where > I'm adding some background information about the dataset and the method > used. > > I used the test case example. > If you get time can you please check my approach and let me know if I'm > going in the wrong direction. > > Thanks, > Abhinav > > On Sat, Apr 16, 2016 at 2:52 AM, Abhinav Gupta <[email protected]> > wrote: > >> Hi Marcus, >> So I'll get started with the Internet Advertisements dataset and >> implement FFN for it, followed by Convolututional Network for it and FFN >> for Human Activity Recognition Using Smartphones dataset. I'm sharing a doc >> with you where I'll get started with the implementation of the first task >> (implementing FFN for Internet Advertisements dataset). >> >> As I'm new to the process please let me know if you feel that I should >> change my methodology at some points. >> >> Thanks, >> Abhinav >> >> On Thu, Apr 14, 2016 at 11:11 PM, Marcus Edel <[email protected]> >> wrote: >> >>> Hello Abhinav, >>> >>> nice to hear from you. You found some really interesting dataset and >>> except for >>> the "Anonymous Microsoft Web Data Data Set" which is a categorical >>> dataset, we >>> can definitely use the datasets for some neat examples. I like that none >>> of the >>> datasets looks like the other. So we could show e.g that a convolution >>> neural >>> network is the preferred model for the Internet Advertisements Data Set >>> as like >>> maybe a standard feed-forward network. I guess, we should start with one >>> of the >>> datasets first and see how things go, what do you think? >>> >>> Thanks again, for taking the time to look into this. >>> >>> Thanks, >>> Marcus >>> >>> On 14 Apr 2016, at 00:58, Abhinav Gupta <[email protected]> >>> wrote: >>> >>> Hi Marcus, >>> Sorry for the late response. >>> I'hv shortlisted these datasets, please have a look at them. >>> http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements >>> >>> >>> http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones >>> >>> http://archive.ics.uci.edu/ml/datasets/Anonymous+Microsoft+Web+Data >>> >>> http://archive.ics.uci.edu/ml/datasets/Tic-Tac-Toe+Endgame >>> >>> Please let me know if you find any one of them appropriate to mention in >>> the example or if you have some comments regarding the datasets. In the >>> meanwhile I'll be looking for more. >>> >>> Thanks, >>> Abhinav >>> >>> On Mon, Apr 11, 2016 at 6:11 AM, Marcus Edel <[email protected]> >>> wrote: >>> >>>> Hello Abhinav, >>>> >>>> sounds great to me. The UCI repository is probably a good start: >>>> https://archive.ics.uci.edu/ml/datasets.html Also sharing a doc so >>>> that we can >>>> work together on it is a good idea. I look forward to hearing from you. >>>> >>>> Thanks, >>>> Marcus >>>> >>>> On 10 Apr 2016, at 10:01, Abhinav Gupta <[email protected]> >>>> wrote: >>>> >>>> Hi Marcus, >>>> So I'll look for some datasets, make a list and will share them >>>> with you. Once we settle on the dataset I'll share a doc with the >>>> information about model, dataset and the example like you mentioned. And I >>>> think representing dataset with few neat images can be taken care of while >>>> choosing the dataset. >>>> >>>> Please let me know if it sounds good to you. >>>> >>>> Thanks, >>>> Abhinav >>>> >>>> On Sat, Apr 9, 2016 at 4:58 AM, Marcus Edel <[email protected]> >>>> wrote: >>>> >>>>> Hello Abhinav, >>>>> >>>>> I think it would be a good idea, to include some examples and to >>>>> explain the >>>>> examples step by step; probably with some sort of background >>>>> information about >>>>> the model and the dataset used. Maybe we can come up with some neat >>>>> images, for >>>>> the dataset. If you like, you can start with that, don't feel >>>>> obligated to do >>>>> so. Also, if you don't like the idea, that's also fine, in this case, >>>>> we can >>>>> come up with other ideas. >>>>> >>>>> Also, I'm not sure we should use the thyroid dataset for the examples, >>>>> it's kind >>>>> of an uninteresting dataset, don't you think? Don't get me wrong, the >>>>> dataset is >>>>> great and important, but maybe we can find something fancy. >>>>> >>>>> Thanks, >>>>> Marcus >>>>> >>>>> On 07 Apr 2016, at 08:09, Abhinav Gupta <[email protected]> >>>>> wrote: >>>>> >>>>> Hii Marcus, >>>>> Sure I would love to collaborate with you. I'hv started going >>>>> through amf's documentation. >>>>> >>>>> I'hv successfully run the test cases as independent examples like I >>>>> mentioned in the issue: https://github.com/mlpack/mlpack/issues/562 >>>>> If you like we can include those, or come up with more different examples. >>>>> >>>>> Please let me know the procedure you would like me to follow, to >>>>> collaborate in an efficient manner. >>>>> >>>>> Thanks, >>>>> Abhinav >>>>> >>>>> >>>>> On Wed, Apr 6, 2016 at 3:57 AM, Marcus Edel <[email protected]> >>>>> wrote: >>>>> >>>>>> Hello Abhinav, >>>>>> >>>>>> sorry for the slow response. We think that the best documentation is >>>>>> written by >>>>>> the person who wrote the code. That doesn't mean, we don't appreciate >>>>>> any help >>>>>> with the documentation. I guess the person who wrote the code, has a >>>>>> different >>>>>> view on what might be helpful and what is trivial as another user. >>>>>> So, if you >>>>>> like we can combine both views and write a strong documentation, what >>>>>> do you >>>>>> think? >>>>>> >>>>>> The documentation should be based on the already existing tutorials, >>>>>> you could >>>>>> take a look at the amf tutorial. I think, it would be nice to include >>>>>> some neat >>>>>> examples. >>>>>> >>>>>> Thanks, >>>>>> Marcus >>>>>> >>>>>> > On 03 Apr 2016, at 09:11, Abhinav Gupta <[email protected]> >>>>>> wrote: >>>>>> > >>>>>> > Hi Ryan, Marcus , >>>>>> > I'm interested in writing a basic documentation on how to use >>>>>> mlpack to build neural networks along with few basic examples (derived >>>>>> mostly from the test cases). If someone is already working on this I >>>>>> would >>>>>> love to collaborate with him/her. >>>>>> > Also is there any standard procedure that you would like me to >>>>>> follow ? >>>>>> > >>>>>> > Thanks, >>>>>> > Abhinav >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >
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