Unsupervised Deep Learning in Python

• 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 and Tensorflow
• Understand how stacked autoencoders are used in deep learning
• Write a stacked denoising autoencoder in Theano and Tensorflow
• 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 and Tensorflow
• Visualize and interpret the features learned by autoencoders and RBMs
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

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, andPython coding. You'll want to install Numpy,Theano, and Tensorflowfor this course. These are essential items in yourdata 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 wantmorethan just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

• Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

• My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

• Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

• After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

• calculus

• linear algebra

• probability

• Python coding: if/else, loops, lists, dicts, sets

• Numpy coding: matrix and vector operations, loading a CSV file

• can write a feedforwardneural network in Theano or Tensorflow

WHATORDERSHOULDITAKEYOURCOURSESIN?:

• Check out the lecture "Machine Learning and AIPrerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)