Deep Learning: GANs and Variational Autoencoders

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Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow.

Includes:
5.5 hours on-demand video
Full lifetime access
Access on mobile and TV
Certificate of Completion

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. =)

NOTES:

All the code for this course can be downloaded from my github:

/lazyprogrammer/machine_learning_examples

In the directory: unsupervised_class3

Make sure you always “git pull” so you have the latest version!

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.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)
Linear Regression in Python
Logistic Regression in Python
(Supervised Machine Learning in Python)
(Bayesian Machine Learning in Python: A/B Testing)
Deep Learning in Python
Practical Deep Learning in Theano and TensorFlow
(Supervised Machine Learning in Python 2: Ensemble Methods)
Convolutional Neural Networks in Python
(Easy NLP)
(Cluster Analysis and Unsupervised Machine Learning)
Unsupervised Deep Learning
(Hidden Markov Models)
Recurrent Neural Networks in Python
Artificial Intelligence: Reinforcement Learning in Python
Natural Language Processing with Deep Learning in Python
Advanced AI: Deep Reinforcement Learning in Python
Deep Learning: GANs and Variational Autoencoders
Who is the target audience?
Anyone who wants to improve their deep learning knowledge

 

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