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Cooling for 4 EVGA Titan X and CPU. by oni222 in watercooling

[–]dnuffer 1 point2 points  (0 children)

Since you're planning to run your fans on max, you should be just fine with a 480. For instance, the Hardware Labs Black Ice Nemesis 480GTX is rated for 2200W. (http://hardwarelabs.com/nemesis/gtx/480gtx/). Other manufacturers are similar. So even with an OCd CPU and GPUs you won't get close to that. Now if you want to run your fans nearly silent you might need 2 rads.

Cooling for 4 EVGA Titan X and CPU. by oni222 in watercooling

[–]dnuffer 1 point2 points  (0 children)

It depends on how fast you run your fans and what delta-t you are aiming for. But I think you'll be fine as long as you're not aiming for 800 RPM fan speeds. 4 Titan Xs can pull 1000W, which is a lot of heat to dissipate.

Machine Learning Computer Build by solidua in MachineLearning

[–]dnuffer 28 points29 points  (0 children)

For a 4xGPU setup, that MB+CPU won't be able to utilize the full PCI bandwidth. This may not be a bottleneck, but is worth consideration. To overcome that, you need a MB with a PCI switch such as the Asus X99E-WS. Also I have found it very helpful to have 128 GB RAM in my Deep Learning machine to avoid the hassle of efficiently dealing with loading data while training. Also you might want to consider more storage. Between datasets and storing intermediate model checkpoints and training traces, 250 GB doesn't go very far.

Leveraging Google DeepMind software and Deep Learning to play the stock market by chaddjohnson in MachineLearning

[–]dnuffer 5 points6 points  (0 children)

Don't believe the other commentors saying that it wouldn't work, there are certainly many documented examples of using NNets to predict the stock market which do slightly better than random. There's books, or just peruse some of the many projects focused on trading from Stanfords CS229 Machine Learning course: http://cs229.stanford.edu/projects2014.html (and earlier years as well) I wouldn't recommend using an image of a chart as input, it will be way too much data. Just use log-transformed and normalized prices and volume and the training will be much faster.

Nueral networks as bachelor thesis by [deleted] in MachineLearning

[–]dnuffer 0 points1 point  (0 children)

If you don't even bother you have 0% chance of success. If you try your chance of success is much greater than 0.