updated image link
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"\n",
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"\n",
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"Here's a overview of the architecure:\n",
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"Here's a overview of the architecure:\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"First, we'll define a helper to set up a convolutional block in the network, `blockUNet`. Note, we don't use any pooling! Instead we use strides and transpose convolutions (these need to be symmetric for the decoder part, i.e. have an uneven kernel size), following [best practices](https://distill.pub/2016/deconv-checkerboard/). The full pytroch neural network is managed via the `DfpNet` class."
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"First, we'll define a helper to set up a convolutional block in the network, `blockUNet`. Note, we don't use any pooling! Instead we use strides and transpose convolutions (these need to be symmetric for the decoder part, i.e. have an uneven kernel size), following [best practices](https://distill.pub/2016/deconv-checkerboard/). The full pytroch neural network is managed via the `DfpNet` class."
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]
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]
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@ -808,4 +808,4 @@
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 0
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}
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}
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