> The source of AlphaGo Zero is really of zero interest (pun intended). The source code is the first-hand account of how it works, whereas an academic paper is a second-hand account. So, definitely not zero use.
> So yes, the database of 29M self-play games would be immensely more > valuable. (Probably like the last 5M or so is fine, too). I prefer the > games over the network - with the games it's easier to train a smaller > network that gives better results on PC's that don't have 4 TPUs in them. Does anyone know of research/code on the topic of reducing the size/complexity of deep learning networks? I think it should be possible to reduce either the number of layers, or the size of each layer, with only a small drop in accuracy, but it seems like the two fully-connected networks at the top will then need retraining? However, this article is showing results, beyond what I thought would be possible, even on the very deep image networks: https://www.oreilly.com/ideas/compressing-and-regularizing-deep-neural-networks BTW, I notice his PhD thesis has just been published. Might have to add it to my reading list: http://stanford.edu/~songhan/ Darren _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
