> especially if it had a relevant GPU For basic learning on somewhat larger datasets (sub-TB), I have found that my 750 TI SC GPU has been very adequate; the o

> especially if it had a relevant GPU For basic learning on somewhat larger datasets (sub-TB), I have found that my 750 TI SC GPU has been very adequate; the only time I maxed it out was while doing "live training" of a tensorflow CNN from images generated from a driving simulator - so I was using part of the GPU for 3D rendering, and part of it for the training. This was for some "self-driving vehicle" experimentation... But running the model against the same simulation (that is, having the model drive instead of watching me drive and learning) actually used much less GPU; while I wasn't getting stellar frame rates in either case, they were adequate for the purpose. I'm just mentioning it, because many people assume you have to have a crazy amount of compute power with a GPU for ML tasks right off the bat (and the associated higher costs of such a system), when in reality that may not be the case (depending on what you are doing and how you are doing it), and you can get away a lesser system. Where I ran into problems with my GPU, though, was in the case of training using larger datasets (that is, non-streaming) - I couldn't fit them all in the GPU's memory (I think the card only has 1 GB), and so I had to do a batch system - and while it worked well, it was a lot slower because of shuffling data to and from the GPU. But for my learning purposes, it still did a fine job IMHO; of course, if I were doing such tasks for actual analysis or other "professional" (and paying) purposes, a better card would definitely be in order.