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Deep Learning: GANs and Variational Autoencoders
Introduction and Outline
Welcome (4:37)
Special Offer! Get the VIP version of this course (1:14)
Where does this course fit into your deep learning studies? (5:00)
Where to get the code and data (3:51)
How to succeed in this course (5:19)
Generative Modeling Review
What does it mean to Sample? (4:57)
Sampling Demo: Bayes Classifier (3:57)
Gaussian Mixture Model Review (10:31)
Sampling Demo: Bayes Classifier with GMM (3:54)
Why do we care about generating samples?
Neural Network and Autoencoder Review (7:26)
Tensorflow Warmup (4:07)
Theano Warmup (4:54)
Variational Autoencoders
Variational Autoencoders Section Introduction (5:39)
Variational Autoencoder Architecture (5:57)
Parameterizing a Gaussian with a Neural Network (8:00)
The Latent Space, Predictive Distributions and Samples (5:13)
Cost Function (7:28)
Tensorflow Implementation (pt 1) (7:18)
Tensorflow Implementation (pt 2) (2:29)
Tensorflow Implementation (pt 3) (9:55)
The Reparameterization Trick (5:05)
Theano Implementation (10:52)
Visualizing the Latent Space (3:09)
Bayesian Perspective (3:09)
Variational Autoencoder Section Summary (4:02)
Generative Adversarial Networks (GANs)
GAN - Basic Principles (5:13)
GAN Cost Function (pt 1) (7:23)
GAN Cost Function (pt 2) (4:56)
DCGAN (7:38)
Batch Normalization Review (8:01)
Fractionally-Strided Convolution (8:35)
Tensorflow Implementation Notes (13:23)
Tensorflow Implementation (18:13)
Theano Implementation Notes (7:26)
Theano Implementation (19:47)
GAN Summary (9:43)
Appendix
How to How to install Numpy, Theano, Tensorflow, etc... (17:32)
How to Succeed in this Course (Long Version) (5:55)
How to Code by Yourself (part 1) (15:54)
How to Code by Yourself (part 2) (9:23)
BONUS: Where to get discount coupons and FREE deep learning material (5:31)
Gaussian Mixture Model Review
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