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Copy file name to clipboardExpand all lines: README.md
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## Workloads
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This paper contains a full description of the workloads, performance characteristics, and the rationale behind the project:
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This paper contains a description of the workloads, performance characteristics, and the rationale behind the project:
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> R. Adolf, S. Rama, B. Reagen, G.Y. Wei, D. Brooks. "Fathom: Reference Workloads for Modern Deep Learning Methods."
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[(Arxiv)](http://arxiv.org/abs/1608.06581)
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AlexNet | Image classifier. Watershed for deep learning by beating hand-tuned image systems at ILSVRC 2012.
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DeepQ | Atari-playing neural network from DeepMind. Achieves superhuman performance on majority of Atari2600 games, without any preconceptions.
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## Data
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Fathom does not come with datasets suitable for training. This is a combination of size (realistic training sets are often massive) and licensing (an oft-repeated mantra is that good data is more valuable than a good model).
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Regardless, the inputs Fathom is designed for are standard and widely-available.
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These links should take you to the original data owners:
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-[ImageNet](http://www.image-net.org/download-images) - requires registration, but downloads are free for non-commercial purposes.
- TIMIT - requires membership of the Linguistic Data Consortium (this is not free, but it is widely available in the research community).
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- Atari "Breakout" ROM - available online
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## Getting Started
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We've begun to put together some actual documentation, and it's already better than what we had: [Fathom Quickstart Guide](http://fathom.readthedocs.io/en/latest/quickstart/).
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We've begun to put together some actual documentation, and it's already better than what we had:
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