文件名称：深度生成模型
文件大小：46.46MB
文件格式：ZIP
更新时间：2021-12-01 14:13:38
GAN 机器学习 生成模型
cs236课件，Generative models are widely used in many subfields of AI and Machine Learning.Recent advances in parameterizing these models using deep neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech
【文件预览】：
cs236-Deep Generative Models
----cs236_lecture9Generative Adversarial Networks.pdf(5.46MB)
----cs236_lecture3Autoregressive Models.pdf(3.73MB)
----cs236_lecture1deepgenerativemodels.pdf(12.98MB)
----cs236_lecture10Generative Adversarial Networks.pdf(3.99MB)
----cs236_lecture11Evaluating Generative Models.pdf(5.14MB)
----cs236_lecture2Representation.pdf(1.68MB)
----cs236_lecture5Latent Variable Models.pdf(1.56MB)
----cs236_lecture4Maximum Likelihood Learning.pdf(872KB)
----cs236_lecture7Normalizing Flow Models.pdf(2.84MB)
----cs236_lecture6Latent Variable Models.pdf(956KB)
----cs236_lecture12Variants and Combinations of Basic Models.pdf(1.6MB)
----session3Deep Learning Primer.pdf(1.45MB)
----cs236_lecture8Normalizing Flow Models.pdf(3.48MB)
----cs236_lecture13Energy Based Models.pdf(1.59MB)