March 2019
Intermediate to advanced
576 pages
20h 51m
Portuguese (Portugal, Brazil)
Content preview from Mãos à Obra: Aprendizado de Máquina com Scikit-Learn e TensorFlow
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Autoencoders Variacionais | 451
latente que faz com que o autoencoder tenha codificações que parecem ter sido amostradas a
partir de uma distribuição Gaussiana simples, para a qual utilizamos a divergência KL entre
a distribuição de destino (a distribuição Gaussiana) e a distribuição real das codificações.
A matemática é um pouco mais complexa do que antes, principalmente por causa do ruído
Gaussiano que limita a quantidade de informação que pode ser transmitida à camada de
codificação (empurrando assim o autoencoder para aprender características úteis). Feliz-
mente, as equações simplificam o código a seguir para a perda latente:
7
eps = 1e-10 ...
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