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Deep Learning is very computationally intensive, so you will need a fast CPU with many cores, right? Or is it maybe wasteful to buy a fast CPU? One of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. Here I will guide you step by step through the hardware you will need for a cheap high performance system. In my work on parallelizin
I have been using GPUs for nearly two years now and for me it is again and again amazing to see how much speedup you get. Compared to CPUs 20x speedups are typical, but on larger problems one can achieve 50x speedups. With GPUs you can try out new ideas, algorithms and experiments much faster than usual and get almost immediate feedback as to what works and what does not. This is a very important
When I started using GPUs for deep learning my deep learning skills improved quickly. When you can run experiments of algorithms and algorithms with different parameters and gain rapid feedback you can just learn much more quickly. At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get
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