Unsupervised Deep Learning in Python

Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA

What you'll learn
- 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

Requirements
- Knowledge of calculus and linear algebra
- Python coding skills
- Some experience with Numpy, Theano, and Tensorflow
- Know how gradient descent is used to train machine learning models
- Install Python, Numpy, and Theano
- Some probability and statistics knowledge
- Code a feedforward neural network in Theano or Tensorflow

Description
Ever wondered how AI technologies likeOpenAIChatGPT,GPT-4,DALL-E,Midjourney, andStable Diffusionreally work? In this course, you will learn the foundations of these groundbreaking applications.

This course is the next logical step in mydeep learning, data science,andmachine learningseries. I’ve done a lot of courses about deep learning, and I just released a course aboutunsupervised learning, where I talked aboutclusteringanddensity 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 ast-SNE (t-distributed stochastic neighbor embedding).

Next, we’ll look at a special type of unsupervised neural network called theautoencoder. 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 superviseddeep neural network. Autoencoders are like a non-linear form of PCA.

Last, we’ll look atrestricted Boltzmann machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders topretrainyour supervised deep neural network. I’ll show you an interesting way of training restricted Boltzmann machines, known asGibbs sampling, a special case ofMarkov 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 asContrastive DivergenceorCD-k. As in physical systems, we define a concept calledfree energyand 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, andPythoncoding. You'll want to installNumpy,Theano, and Tensorflowfor this course. These are essential items in yourdata analyticstoolbox.

If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plainbackpropagation, 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 feedforward neural network in Theano or Tensorflow

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

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

Who this course is for:
- 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
More Info