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numpy_ml.bandits.policies | ||
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numpy_ml.bandits.trainer | ||
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:undoc-members: | ||
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# Modules | ||
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The `modules.py` module implements common multi-layer blocks that appear across | ||
many modern deep networks. It includes: | ||
many modern deep networks. It includes: | ||
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- Bidirectional LSTMs ([Schuster & Paliwal, 1997](https://pdfs.semanticscholar.org/4b80/89bc9b49f84de43acc2eb8900035f7d492b2.pdf)) | ||
- ResNet-style "identity" (i.e., `same`-convolution) residual blocks ([He et al., 2015](https://arxiv.org/pdf/1512.03385.pdf)) | ||
- ResNet-style "convolutional" (i.e., parametric) residual blocks ([He et al., 2015](https://arxiv.org/pdf/1512.03385.pdf)) | ||
- WaveNet-style residual block with dilated causal convolutions ([van den Oord et al., 2016](https://arxiv.org/pdf/1609.03499.pdf)) | ||
- Transformer-style multi-headed dot-product attention ([Vaswani et al., 2017](https://arxiv.org/pdf/1706.03762.pdf)) | ||
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