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Unsupervised Deep Learning in Python

Name: Unsupervised Deep Learning in Python
Rating: 4.6 (2475 reviews)

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

Created byLazy Programmer Team, Lazy Programmer Inc.

Last updated 6/2025

English

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

Course content

14 sections • 78 lectures • 10h 2m total length

Introduction and Outline7:27
How to Succeed in this Course3:04
Where to get the code and data5:02
Clone the course code from GitHub with git clone, avoid forking, and use the eminence dataset from Kaggle with the labeled file in a large files folder for hands-on learning.
Tensorflow or Theano - Your Choice!4:09
What are the practical applications of unsupervised deep learning?5:34
Where does this course fit into your deep learning studies?2:57
Explore how this course fits into your deep learning studies by connecting linear and logistic regression with supervised and unsupervised concepts like backpropagation, softmax, momentum, and clustering.

What does PCA do?4:32
Pca stands for principal components analysis and helps transform high-dimensional data into useful features for downstream algorithms, and also enables visualization in two or three dimensions.
How does PCA work?11:21
Why does PCA work? (PCA derivation)10:12
PCA only rotates5:29
MNIST visualization, finding the optimal number of principal components3:39
PCA implementation3:29
PCA for NLP3:37
Learn how PCA reduces dimensionality in natural language processing by capturing redundancy and correlation in text data, with practical theory and code.
PCA objective function2:05
PCA Application: Naive Bayes9:51
Apply PCA to decorrelate features and enable a gaussian naive Bayes classifier to model p(x|y) and p(y) using Bayes' rule, improving handwritten digit classification.
SVD (Singular Value Decomposition)10:58
Suggestion Box3:10

Autoencoders3:20
Denoising Autoencoders1:55
Stacked Autoencoders3:32
Writing the autoencoder class in code (Theano)11:55
Testing our Autoencoder (Theano)3:05
Writing the deep neural network class in code (Theano)12:42
Autoencoder in Code (Tensorflow)8:29
Testing greedy layer-wise autoencoder training vs. pure backpropagation3:33
Compare greedy layer-wise autoencoder pre-training with pure backpropagation in unsupervised deep learning using Python, showing faster convergence with pre-training and the use of squared error and cross-entropy losses.
Cross Entropy vs. KL Divergence4:39
Deep Autoencoder Visualization Description1:32
Deep Autoencoder Visualization in Code11:14
An Autoencoder in 1 Line of Code4:50
Explore unsupervised deep learning in Python with a one-line autoencoder that learns nonlinear latent representations, showing PCA objectives align with autoencoders and the benefits of greedy layer wise pre-training.

Basic Outline for RBMs4:51
Explore how restricted Boltzmann machines use greedy layer-wise pre-training to uncover compact latent representations. See how they relate to auto encoders for removing redundancy and noise in data.
Introduction to RBMs8:21
Motivation Behind RBMs6:51
Intractability3:11
Neural Network Equations7:43
Training an RBM (part 1)11:34
Demonstrates training an rbm with maximum likelihood, using the free energy to substitute intractable sums, and applies gradient descent with a positive phase for observed data and a negative phase for others.
Training an RBM (part 2)6:18
Training an RBM (part 3) - Free Energy7:20
RBM Greedy Layer-Wise Pretraining4:50
Explore greedy layer-wise pretraining with restricted Boltzmann machines to overcome vanishing gradients and build supervised networks by stacking RBMs into hidden representations and adding a logistic regression layer with fine-tuning.
RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST14:24
RBM in Code (Tensorflow)5:03

Application of PCA and SVD to NLP (Natural Language Processing)2:30
We use SVD to visualize the words in book titles. You'll see how related words can be made to appear close together in 2 dimensions using the SVD transformation.
Latent Semantic Analysis in Code10:08
Application of t-SNE + K-Means: Finding Clusters of Related Words8:38
Apply t-sne and k-means to identify clusters of related words from a tf-idf term-document matrix, visualize them in two dimensions, and inspect the resulting word clusters.

Recommender Systems Section Introduction12:30
Why Autoencoders and RBMs work5:58
Explore how autoencoders and RBMs learn latent representations to fill in missing data and generate recommendations, using patterns in user ratings and genres to predict preferences.
Data Preparation and Logistics5:33
Data Preprocessing Code15:26
Cover data preprocessing for movie ratings, converting IDs to zero-based indices and shrinking to active users and movies. Build lookup dictionaries and perform a train-test split, saving results with pickle.
AutoRec10:14
AutoRec in Code11:45
Categorical RBM for Recommender System Ratings11:32
Recommender RBM Code pt 17:26
Recommender RBM Code pt 24:16
Recommender RBM Code pt 311:42
Build and train a restricted Boltzmann machine for recommender systems in Python, detailing data loading, RBM class, training loop, free energy objective, and MSE evaluation.
Recommender RBM Code Speedup7:53

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 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, 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.

"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

Unsupervised Deep Learning in Python

What you'll learn

Explore related topics

Course content

Introduction and Outline6 lectures • 28min

Principal Components Analysis11 lectures • 1hr 8min

t-SNE (t-distributed Stochastic Neighbor Embedding)5 lectures • 22min

Autoencoders12 lectures • 1hr 11min

Restricted Boltzmann Machines11 lectures • 1hr 20min

The Vanishing Gradient Problem2 lectures • 15min

Applications to NLP (Natural Language Processing)3 lectures • 21min

Applications to Recommender Systems11 lectures • 1hr 44min

Theano and Tensorflow Basics Review4 lectures • 34min

Appendix / FAQ Finale1 lecture • 4min

Requirements

Description

Who this course is for: