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I have a pretty good understanding of basic concepts in neural networks. I'm looking to replicate the findings of a paper to get a better understanding of some more advanced concepts. I'm open to anything from momentum, deep networks, RNNs, etc.

Any suggestions on which papers would be best for this, or even for reading. Google wasn't all that helpful.

Bonus points for open datasets and free publications.

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http://gitxiv.com/ is pretty dedicated to hosting open papers, their code and occasionally a virtual environment hosted online with the required dependencies.

There's also assignments hosted online by Stanford for courses in convolutional neural networks for image recognition and recurrent neural networks for natural language processing.

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Steps

Firstly, you can learn it from books, alternativaly, you can get online courses from MIT, Standford, caltech.(A simples search on google to get materials of this courses)

Secondaly, you should choose what the programming language you would like to learn this concepts.

Getting Start with databases

So, in R(subjetive choose) have a package that have many databases for training your model.

Besides, the University of California, Irvine maintain a repository of 333 dataset to learn machine learning.

UCI Machine Learning Repository

Books

  • Machine Learning Paradigms

  • Big Data: a primer

  • Documentation of programming language that you are working

  • deep learning tutorial

  • Bengio, Yoshua (2009). "Learning Deep Architectures for AI". Foundations and Trends in Machine Learning 2

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