Deep Learning: GANs and Variational Autoencoders
4.7 (1,045 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,207 students enrolled

Deep Learning: GANs and Variational Autoencoders

Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow
4.7 (1,045 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
8,207 students enrolled
Last updated 10/2018
English
English [Auto-generated], Portuguese [Auto-generated]
Current price: $9.99 Original price: $179.99 Discount: 94% off
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This course includes
  • 7.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn the basic principles of generative models
  • Build a variational autoencoder in Theano and Tensorflow

  • Build a GAN (Generative Adversarial Network) in Theano and Tensorflow

Requirements
  • Know how to build a neural network in Theano and/or Tensorflow
  • Probability
  • Multivariate Calculus
  • Numpy, etc.
Description

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently.

Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.

GAN stands for generative adversarial network, where 2 neural networks compete with each other.

What is unsupervised learning?

Unsupervised learning means we’re not trying to map input data to targets, we’re just trying to learn the structure of that input data.

Once we’ve learned that structure, we can do some pretty cool things.

One example is generating poetry - we’ve done examples of this in the past.

But poetry is a very specific thing, how about writing in general?

If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines.

But what if we go back to poetry and take away the words?

Well then we get art, in general.

By learning the structure of art, we can create more art.

How about art as sound?

If we learn the structure of music, we can create new music.

Imagine the top 40 hits you hear on the radio are songs written by robots rather than humans.

The possibilities are endless!

You might be wondering, "how is this course different from the first unsupervised deep learning course?"

In this first course, we still tried to learn the structure of data, but the reasons were different.

We wanted to learn the structure of data in order to improve supervised training, which we demonstrated was possible.

In this new course, we want to learn the structure of data in order to produce more stuff that resembles the original data.

This by itself is really cool, but we'll also be incorporating ideas from Bayesian Machine Learning, Reinforcement Learning, and Game Theory. That makes it even cooler!

Thanks for reading and I’ll see you in class. =)



HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • Calculus

  • Probability

  • Object-oriented programming

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations

  • Linear regression

  • Gradient descent

  • Know how to build a feedforward and convolutional neural network in Theano and TensorFlow


TIPS (for getting through the course):

  • Watch it at 2x.

  • Take handwritten notes. This will drastically increase your ability to retain the information.

  • Write down the equations. If you don't, I guarantee it will just look like gibberish.

  • Ask lots of questions on the discussion board. The more the better!

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don't just sit there and look at my code.


WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:
  • Anyone who wants to improve their deep learning knowledge
Course content
Expand all 53 lectures 07:36:31
+ Generative Modeling Review
8 lectures 51:06
What does it mean to Sample?
04:57
Sampling Demo: Bayes Classifier
03:57
Gaussian Mixture Model Review
10:31
Sampling Demo: Bayes Classifier with GMM
03:54
Why do we care about generating samples?
11:20
Neural Network and Autoencoder Review
07:26
Tensorflow Warmup
04:07
Theano Warmup
04:54
+ Variational Autoencoders
13 lectures 01:25:18
Variational Autoencoder Architecture
05:57
Parameterizing a Gaussian with a Neural Network
08:00
The Latent Space, Predictive Distributions and Samples
05:13
Cost Function
07:28
Tensorflow Implementation (pt 1)
07:18
Tensorflow Implementation (pt 2)
02:29
Tensorflow Implementation (pt 3)
09:55
The Reparameterization Trick
05:05
Theano Implementation
10:52
Visualizing the Latent Space
03:09
Bayesian Perspective
10:11
Variational Autoencoder Section Summary
04:02
+ Generative Adversarial Networks (GANs)
11 lectures 01:51:50
GAN - Basic Principles
05:13
GAN Cost Function (pt 1)
07:23
GAN Cost Function (pt 2)
06:28
DCGAN
07:38
Batch Normalization Review
08:01
Fractionally-Strided Convolution
08:35
Tensorflow Implementation Notes
13:23
Tensorflow Implementation
18:13
Theano Implementation Notes
07:26
Theano Implementation
19:47
GAN Summary
09:43
+ Theano and Tensorflow Basics Review
4 lectures 34:14
(Review) Theano Basics
07:47
(Review) Theano Neural Network in Code
09:17
(Review) Tensorflow Basics
07:27
(Review) Tensorflow Neural Network in Code
09:43
+ Appendix
13 lectures 02:35:20
What is the Appendix?
02:48
Windows-Focused Environment Setup 2018
20:20
How to How to install Numpy, Theano, Tensorflow, etc...
17:32
How to Succeed in this Course (Long Version)
10:24
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Where to get discount coupons and FREE deep learning material
02:20
Python 2 vs Python 3
04:38
Is Theano Dead?
10:03
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07