The direct-to-network stuff is very cool and useful in this scenario. I should point out that for those of you experimenting with deep learning, you'll probably

The direct-to-network stuff is very cool and useful in this scenario. I should point out that for those of you experimenting with deep learning, you'll probably not be writing your own code from scratch. There are various open source libraries (pylearn2, torch, caffe, others) that make things a lot easier when you're getting started. They still have something of a learning curve though. I should caution also that not all of the libraries work as well with the latest or earliest GPUs, so the model of GPU you buy still makes a big difference. And it should be NVIDIA--the deep learning community has largely standardized around their hardware. This is a state of affairs that is constantly changing. Pertinent self promotion: my company (http://www.ersatzlabs.com http://www.ersatzlabs.com) provides a cloud GPU deep learning solution, which I'd argue is an even easier way to get started with deep learning, particularly in visualization and prototyping phases. But anyway, if anyone's curious about deep learning and just getting their feet wet, I'm always happy to talk about it, my email is in my profile.