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Fix typo #8

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2 changes: 1 addition & 1 deletion README.md
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Image super-resolution through deep learning. This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net.

Here's an random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth.
Here's a random, non cherry-picked, example of what this network can do. From left to right, the first column is the 16x16 input image, the second one is what you would get from a standard bicubic interpolation, the third is the output generated by the neural net, and on the right is the ground truth.

![Example output](srez_sample_output.png)

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