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. =)
All the code for this course can be downloaded from my github:
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:
TIPS (for getting through the course):
USEFUL COURSE ORDERING:
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.
I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.