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Thursday, April 14, 2016

Numerai stuff.

We all know and love Kaggle, but it has a number of faults, the worst of which in my eyes is the dirty data which gets dumped there. People who are good at AI, I mean people like me (smile) , are often not very good at the mundane but so necessary potato peeling with a blunt knife which Kaggle seems to require to clean the data.

So, to summarise, Numerai seem to provide cleaner data and more winners. You can go to their site, to FastML, or read the Reddit AMA for more info, I will add links in here as Long as the topic interests me.

http://fastml.com/numerai-like-kaggle-but-with-a-clean-dataset-top-ten-in-the-money-and-recurring-payouts/

https://www.reddit.com/r/MachineLearning/comments/3wdr9e/numerai_a_global_ai_tournament_to_predict_the/

http://fastml.com/what-you-wanted-to-know-about-auc/

Numerai seem to be supporting an interesting Pyhon multicore framework called MachineJS which is optimised for Macs.
https://blog.numer.ai/2016/02/25/machineJS

You may wonder how Numerai can afford to give their data away. The answer is they encrypt it first, using a process named homomorphic encryption, which allows one to obfuscate data while preserving one's ability to work with it.
https://medium.com/@Numerai/encrypted-data-for-efficient-markets-fffbe9743ba8#.d1zxehecc


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An interesting link to some recurrent net explanations and examples
http://karpathy.github.io/2015/05/21/rnn-effectiveness/

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