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In my case, I had been running Ubuntu so most of those tips are moot. But to be thorough, I did remove practically any and every resident program that was running on what should have been a zippy Dell Latitude E with a iQM 2. And yet, running a utility that averaged about 5 seconds on my desktop consistently took 30 seconds on my laptop. Except for every once in awhile, when it would take 6 or 7 seconds. Whatever the logic is that determines when to power down was clearly NOT working as intended on my laptop. After disabling Speed Step, I have been running for the entire day at speeds very similar to my desktop. Hopefully someone else thinks to Google for this problem and find themselves helped by a similar approach.

So for example, a RNN could be fed characters of Shakespearean text, learn an internal representation of what Shakespearean text looks like, and then sample its predictions to generate some new Shakespeare. A vanilla RNN inputs a sequence of data, computes some hidden state over time, and then outputs a sequence. This model treats the image features as the first word of a sentence and then recursively predicts the next word until a stop token is predicted. One way to speed up training is to process a batch of training examples at the same time.

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