Hi I've gone through the paper on efficient backpropagation that was provided on the ideas page. I could follow the paper only up to section 5 (Convergence of Gradient Descent), and that is because this is the minimum that is covered in most tutorials on neural networks.
My basic doubt is about whether the literature provided on the ideas page (for this project and for other projects as well) is just a pointer, or are candidates expected to code based explicitly on the literature. If it is the latter, then we will have to spend some time studying the material in depth. But this will be difficult as we will be expected to spend most of time coding during the summer. Or is it the case that we are supposed to familiarize ourselves with the material before the application deadline? If that is the case, I'd like to know how much expertise is needed in this particular project, with respect to the said paper. I'm going to apply for this project under GSoC, and here are my pros and cons. Pros: I'm taking a course in neural networks this semester, so this particular project will have a minimum overhead for me. Although I'm officially taking this course now, in my final undergrad year, but I've been using machine learning techniques for a long time. I'm familiar with MLPs, clustering and the use of linear transformations as feature extraction. I'm somewhat obsessive about my understanding of these tools being very clear. I tend to write everything from scratch and I use available libraries only when I'm sure that I know what they do. For instance, before I use most estimators in sklearn, I write my own code and test it on small datasets and make sure that they produce similar outputs with sklearn estimators. (Of course, the notion of this similarity of results is subjective, I don't know how to establish *rigorously* that my results are similar to those of the estimators in sklearn. For most purposes I rely on least-squared error.) In fact, I was also somewhat surprised that sklearn doesn't begin with neural networks. Then I found out that sklearn is meant for applying machine learning directly, without too much theory. Cons: I've never actively contributed to any open source library, as I've only recently become acquainted with the concept of 'community coding'. I discovered the PEP 8 style only a couple of months ago. I've learnt OOP very recently too, but now that I know it, I've started thinking in terms of classes and their instances almost everywhere in my code (Is that weird?). In fact, my style of coding has been called 'too MATLABish' :) Basically all my education in open-source coding happened after SciPy India 2011, which happened in December. In summary, I'm not very confident about how the community will feel about my code, but I do make sure it does its job. And I can always learn the soft-skills required for contributing to the community. Here's a sample code of a Perceptron class that I wrote anew when I learnt OOP. https://gist.github.com/2188832 Please go through it and be as critical as possible. So does this warrant a GSoC application? Cheers, ------------------------------------------------------------------------------ This SF email is sponsosred by: Try Windows Azure free for 90 days Click Here http://p.sf.net/sfu/sfd2d-msazure _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
