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Oud 13 augustus 2017, 23:42   #37
Nr.10
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Verwijzend naar post 30 uit deze discussie.
In verband met het via deep learning trainen
van NLP systemen voor de Chinese taal.
De enorme rekenkracht die hiervoor nodig is.
Een wetenschapper bij Baidu aan het woord: BRON
Tientallen exaflops om deep speech te trainen.
“As with other deep neural networks, our system gets more and more accurate as it is trained on larger and larger datasets. [Baidu] researchers have been working hard to find large datasets from which our model can learn all the nuances and complexities of spoken Chinese, which is a very diverse language with many dialects and local accents. As we amass these datasets, we encounter interesting systems problems as we try to scale the training of our system.

“To give some context, training Deep Speech on our full Chinese dataset takes tens of exaflops — that’s more than 10 quintillion (billion billion) multiplications and additions. In order to evaluate whether a new neural network or additional data will improve Deep Speech, we have to wait for this training process to converge, which can take quite some time. Accordingly, the more rapidly we can train Deep Speech, the more ideas we can evaluate, and the more rapidly we make progress.

“This is why we pay special attention to systems issues when training our models. We have noticed that as we improve the efficiency of our training system, accuracy improvements follow rather directly. We parallelize the training of our system across multiple GPUs in order to reduce this training time. Our current system sustains more than 26 teraflops while training a single model on 8 GPUs, which allows us to train Deep Speech on a large dataset in a matter of days. We continue pushing the boundaries of scalability, because we’ve observed that our accuracy continues to improve as we scale our training set.”
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