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Development Data Science Python

Unsupervised Deep Learning in Python

Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA
Rating: 4.5 out of 54.5 (1,624 ratings)
17,258 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

14 sections • 82 lectures • 10h 20m total length

  • Preview01:55
  • Preview02:57
  • Preview05:51
  • Where to get the code and data
    05:02
  • Tensorflow or Theano - Your Choice!
    04:09
  • What are the practical applications of unsupervised deep learning?
    05:34

  • What does PCA do?
    Preview04:32
  • How does PCA work?
    11:21
  • Why does PCA work? (PCA derivation)
    10:12
  • PCA only rotates
    05:29
  • MNIST visualization, finding the optimal number of principal components
    03:39
  • PCA implementation
    03:29
  • PCA for NLP
    03:37
  • PCA objective function
    02:05
  • PCA Application: Naive Bayes
    09:51
  • SVD (Singular Value Decomposition)
    10:58
  • Suggestion Box
    03:03

  • t-SNE Theory
    04:28
  • t-SNE Visualization
    04:33
  • t-SNE on the Donut
    05:51
  • t-SNE on XOR
    04:36
  • t-SNE on MNIST
    02:12

  • Autoencoders
    03:20
  • Denoising Autoencoders
    01:55
  • Stacked Autoencoders
    03:32
  • Writing the autoencoder class in code (Theano)
    11:55
  • Testing our Autoencoder (Theano)
    03:05
  • Writing the deep neural network class in code (Theano)
    12:42
  • Autoencoder in Code (Tensorflow)
    08:29
  • Testing greedy layer-wise autoencoder training vs. pure backpropagation
    03:33
  • Cross Entropy vs. KL Divergence
    04:39
  • Deep Autoencoder Visualization Description
    01:32
  • Deep Autoencoder Visualization in Code
    11:14
  • An Autoencoder in 1 Line of Code
    04:50

  • Basic Outline for RBMs
    04:51
  • Introduction to RBMs
    08:21
  • Motivation Behind RBMs
    06:51
  • Intractability
    03:11
  • Neural Network Equations
    07:43
  • Training an RBM (part 1)
    11:34
  • Training an RBM (part 2)
    06:18
  • Training an RBM (part 3) - Free Energy
    07:20
  • RBM Greedy Layer-Wise Pretraining
    04:50
  • RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST
    14:24
  • RBM in Code (Tensorflow)
    05:03

  • The Vanishing Gradient Problem Description
    03:07
  • The Vanishing Gradient Problem Demo in Code
    12:17

  • Exercises on feature visualization and interpretation
    02:07

  • Application of PCA and SVD to NLP (Natural Language Processing)
    02:30
  • Latent Semantic Analysis in Code
    10:08
  • Application of t-SNE + K-Means: Finding Clusters of Related Words
    08:38

  • Recommender Systems Section Introduction
    12:30
  • Why Autoencoders and RBMs work
    05:58
  • Data Preparation and Logistics
    05:33
  • Data Preprocessing Code
    15:26
  • AutoRec
    10:14
  • AutoRec in Code
    11:45
  • Categorical RBM for Recommender System Ratings
    11:32
  • Recommender RBM Code pt 1
    07:26
  • Recommender RBM Code pt 2
    04:16
  • Recommender RBM Code pt 3
    11:42
  • Recommender RBM Code Speedup
    07:53

  • (Review) Theano Basics
    07:47
  • (Review) Theano Neural Network in Code
    09:17
  • (Review) Tensorflow Basics
    07:27
  • (Review) Tensorflow Neural Network in Code
    09:43
  • (Review) Keras Basics
    06:48
  • (Review) Keras in Code pt 1
    06:37
  • (Review) Keras in Code pt 2
    04:26

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

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

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 40,516 Reviews
  • 147,955 Students
  • 14 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,181 Reviews
  • 422,558 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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