Course details
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 networkscompete with each other.
What is unsupervised learning?
Unsupervised learning means were not trying to map input data to targets, were just… + Read More
Course details
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 networkscompete with each other.
What is unsupervised learning?
Unsupervised learning means were not trying to map input data to targets, were just trying to learn the structure of that input data.
Once weve learned that structure, we can do some pretty cool things.
One example is generating poetry - weve 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 Ill 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 andconvolutionalneural 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.
WHATORDERSHOULDITAKEYOURCOURSESIN?:
- 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)
Updated on 14 November, 2018 - Read Less
