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[spelling] architecture -> architectures #2610

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[spelling] architecture -> architectures #2610

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2 changes: 1 addition & 1 deletion chapter_generative-adversarial-networks/dcgan.md
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In :numref:`sec_basic_gan`, we introduced the basic ideas behind how GANs work. We showed that they can draw samples from some simple, easy-to-sample distribution, like a uniform or normal distribution, and transform them into samples that appear to match the distribution of some dataset. And while our example of matching a 2D Gaussian distribution got the point across, it is not especially exciting.

In this section, we will demonstrate how you can use GANs to generate photorealistic images. We will be basing our models on the deep convolutional GANs (DCGAN) introduced in :citet:`Radford.Metz.Chintala.2015`. We will borrow the convolutional architecture that have proven so successful for discriminative computer vision problems and show how via GANs, they can be leveraged to generate photorealistic images.
In this section, we will demonstrate how you can use GANs to generate photorealistic images. We will be basing our models on the deep convolutional GANs (DCGAN) introduced in :citet:`Radford.Metz.Chintala.2015`. We will borrow the convolutional architectures that have proven so successful for discriminative computer vision problems and show how via GANs, they can be leveraged to generate photorealistic images.

```{.python .input}
#@tab mxnet
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