This course is the next logical step in my
deep learning,
data science, and
machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about
unsupervised learning, where I talked about
clustering and
density estimation. So what do you get when you put these 2 together? Unsupervised deep learning!
In these course we’ll start with some very basic stuff -
principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as
t-SNE (t-distributed stochastic neighbor embedding).
Next, we’ll look at a special type of unsupervised neural network called the
autoencoder. After describing how an autoencoder works, I’ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised
deep neural network. Autoencoders are like a non-linear form of PCA.
Last, we’ll look at
Restricted Boltzmann Machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders to
pretrain your supervised deep neural network. I’ll show you an interesting way of training restricted Boltzmann machines, known as
Gibbs sampling, a special case of
Markov Chain Monte Carlo, and I’ll demonstrate how even though this method is only a rough approximation, it still ends up reducing other cost functions, such as the one used for autoencoders. This method is also known as
Contrastive Divergence or
CD-k. As in physical systems, we define a concept called
free energy> and attempt to minimize this quantity.
Finally, we’ll bring all these concepts together and I’ll show you visually what happens when you use PCA and t-SNE on the features that the autoencoders and RBMs have learned, and we’ll see that even without labels the results suggest that a pattern has been found.
All the materials used in this course are FREE. Since this course is the 4th in the deep learning series, I will assume you already know calculus, linear algebra, and
Python coding. You'll want to install
Numpy,
Theano and
Tensorflow for this course. These are essential items in your
data analytics toolbox.
If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain backpropagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system, this course is for you.
This course focuses on
"how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about
"seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
Suggested Prerequisites:
- calculus
- linear algebra
- probability
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- linear regression, logistic regression
- neural networks and backpropagation
- Can write a feedforward neural network in Theano and TensorFlow
Tips for success:
- Use the video speed changer! Personally, I like to watch 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!
- Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
- Write code yourself, this is an applied course! Don't be a "couch potato".