Video 1.5 hours
Skill Level Intermediate Level
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Certificate of Completion
Supercharge deep neural networks with unsupervised learning, dimensionality reduction, and visualization using Theano
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 and Theano 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.
Understand the theory behind principal components analysis (PCA)
Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising
Derive the PCA algorithm by hand
Write the code for PCA
Understand the theory behind t-SNE
Use t-SNE in code
Understand the limitations of PCA and t-SNE
Understand the theory behind autoencoders
Write an autoencoder in Theano
Understand how stacked autoencoders are used in deep learning
Write a stacked denoising autoencoder in Theano
Understand the theory behind restricted Boltzmann machines (RBMs)
Understand why RBMs are hard to train
Understand the contrastive divergence algorithm to train RBMs
Write your own RBM and deep belief network (DBN) in Theano
Visualize and interpret the features learned by autoencoders and RBMs
Students and professionals looking to enhance their deep learning repertoire
Students and professionals who want to improve the training capabilities of deep neural networks
Students and professionals who want to learn about the more modern developments in deep learning
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