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@@ -49,6 +49,11 @@ MNIST-1D is a core teaching dataset in Simon Prince's [Understanding Deep Learni
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  **The term "shuffle" refers to shuffling the spatial dimension of the dataset, as in [Zhang et al. (2017)](https://arxiv.org/abs/1611.03530).
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  ## Dataset Creation
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  **The term "shuffle" refers to shuffling the spatial dimension of the dataset, as in [Zhang et al. (2017)](https://arxiv.org/abs/1611.03530).
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+ ## Dimensionality reduction
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+ Visualizing the MNIST and MNIST-1D datasets with t-SNE. The well-defined clusters in the MNIST plot indicate that the majority of the examples are separable via a kNN classifier in pixel space. The MNIST-1D plot, meanwhile, reveals a lack of well-defined clusters which suggests that learning a nonlinear representation of the data is much more important to achieve successful classification.
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5e70f6048ce3c604d78fe133/-YhBPH4FNxk5-NHi647Y1.png)
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  ## Dataset Creation
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