Summary: Painting Pictures with Neural Networks Using VAEs
Learn how variational autoencoders use deep neural networks to represent and generate images, explore posterior approximation with variational methods, and implement these models on datasets like MNIST and CIFAR-10 for image reconstruction and creation.
We'll cover the following...
We'll cover the following...
In this chapter, we learned how deep neural networks can be used to create representations of complex data, such as images, that capture more of their variance than traditional dimension reduction techniques, such as PCA. This is demonstrated ...