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I'm using python 3.4 with tensorflow installed. Is it better to use CPU or gpu? What do you recommend? A: The choice between CPU or GPU can be made per problem or task. The very simple way to view this is to view the problem as either "low precision" or "high precision". In the "low precision" case, the CPU is likely to perform better due to the higher throughput. You could also make the case that an algorithm executed on the CPU is unlikely to use a mathematical library (such as numpy/scipy) as well as the fact that a GPU probably has a larger memory than the CPU. For high-precision computations, the GPU has a higher throughput. It is also worth noting that even with a GPU, there is a trade-off in the type of computation that can be done. GPUs are limited by the nature of the computations they can do. For example, on an Intel i7, the maximum speed is limited by the fact that each core can only do single-precision operations. For learning a model from data, the general choice is to use the CPU. As Tensorflow is not yet released for GPUs, the loss of control over the low-level details of the problem and the use of Python as a programming language (rather than a math-heavy language such as C) are benefits that the CPU provides. One exception to this is the use of GPU for NLP. Tensorflow has been released for this purpose and I've seen some nice work using GPUs for this purpose. I have no personal experience, but this is a topic I would also be interested in exploring. Judgments about magnitude, probability, and choice rate with a primary and a secondary option: an fMRI study. The encoding and maintenance of numerical representations are subjects of debate, but the brain 1a423ce670 TaskbarForms Use KeyboardMacro to access your keyboard using only the keyboard or cursor keys. This can be very handy when you need to do a lot of typing quickly or use the mouse for other applications. 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