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I'm curious about the author's experience with ML. He mentioned one of the Kaggle competitions, and from my understanding most of the people doing Kaggle are using R, Python, or some other language which provides a large degree of support for ML type tasks.

I wonder if the author also uses those and CUDA/GPU makes up a relatively small part of his solutions, or whether it's largely done at such a low level. It'd also be interesting to see how some of the other people who place highly in Kaggle competitions do their coding.

1 comments

I mainly use python and sklearn for Kaggle competitions for my initial models. If I understand the problem better I use some of my own deep learning solutions in python (built on gnumpy and cudamat). However, sometimes my own C++/CUDA implementations come in handy, especially if the data set is large.

Other Kaggle competitors that use deep learning mostly use python libraries like pylearn2 and torch7 for their deep learning models (which are also built on CUDA/C++).

In general it is not so easy to use deep learning on problems other than object recognition. So yes, I do not use deep learning in all of my Kaggle competition simply because it is hard to get them to work well. Using different simple models and to then ensemble them yields often better results for the time invested.

Thanks for posting that. It gave me some interesting things to look into.