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30-Day Money-Back Guarantee
Development Data Science Generative Adversarial Networks (GAN)

Deep Learning: GANs and Variational Autoencoders

Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow
Bestseller
Rating: 4.6 out of 54.6 (2,165 ratings)
14,094 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Portuguese [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Learn the basic principles of generative models
  • Build a variational autoencoder in Theano and Tensorflow
  • Build a GAN (Generative Adversarial Network) in Theano and Tensorflow
Curated for the Udemy for Business collection

Course content

9 sections • 54 lectures • 7h 43m total length

  • Preview04:33
  • Preview05:00
  • Preview03:51
  • How to Succeed in this Course
    05:51

  • What does it mean to Sample?
    04:57
  • Sampling Demo: Bayes Classifier
    03:57
  • Gaussian Mixture Model Review
    10:31
  • Sampling Demo: Bayes Classifier with GMM
    03:54
  • Why do we care about generating samples?
    11:20
  • Neural Network and Autoencoder Review
    07:26
  • Tensorflow Warmup
    04:07
  • Theano Warmup
    04:54
  • Suggestion Box
    03:03

  • Variational Autoencoders Section Introduction
    Preview05:39
  • Variational Autoencoder Architecture
    05:57
  • Parameterizing a Gaussian with a Neural Network
    08:00
  • The Latent Space, Predictive Distributions and Samples
    05:13
  • Cost Function
    07:28
  • Tensorflow Implementation (pt 1)
    07:18
  • Tensorflow Implementation (pt 2)
    02:29
  • Tensorflow Implementation (pt 3)
    09:55
  • The Reparameterization Trick
    05:05
  • Theano Implementation
    10:52
  • Visualizing the Latent Space
    03:09
  • Bayesian Perspective
    10:11
  • Variational Autoencoder Section Summary
    04:02

  • GAN - Basic Principles
    05:13
  • GAN Cost Function (pt 1)
    07:23
  • GAN Cost Function (pt 2)
    06:28
  • DCGAN
    07:38
  • Batch Normalization Review
    08:01
  • Fractionally-Strided Convolution
    08:35
  • Tensorflow Implementation Notes
    13:23
  • Tensorflow Implementation
    18:13
  • Theano Implementation Notes
    07:26
  • Theano Implementation
    19:47
  • GAN Summary
    09:43

  • (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

  • Windows-Focused Environment Setup 2018
    20:20
  • How to How to install Numpy, Theano, Tensorflow, etc...
    17:32

  • How to Code by Yourself (part 1)
    15:54
  • How to Code by Yourself (part 2)
    09:23
  • Proof that using Jupyter Notebook is the same as not using it
    12:29
  • Python 2 vs Python 3
    04:38
  • Is Theano Dead?
    10:03

  • How to Succeed in this Course (Long Version)
    10:24
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
    22:04
  • Machine Learning and AI Prerequisite Roadmap (pt 1)
    11:18
  • Machine Learning and AI Prerequisite Roadmap (pt 2)
    16:07

  • What is the Appendix?
    02:48
  • Preview05:31

Requirements

  • Know how to build a neural network in Theano and/or Tensorflow
  • Probability
  • Multivariate Calculus
  • Numpy, etc.

Description

Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently.

Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs.

GAN stands for generative adversarial network, where 2 neural networks compete with each other.

What is unsupervised learning?

Unsupervised learning means we’re not trying to map input data to targets, we’re just trying to learn the structure of that input data.

Once we’ve learned that structure, we can do some pretty cool things.

One example is generating poetry - we’ve done examples of this in the past.

But poetry is a very specific thing, how about writing in general?

If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines.

But what if we go back to poetry and take away the words?

Well then we get art, in general.

By learning the structure of art, we can create more art.

How about art as sound?

If we learn the structure of music, we can create new music.

Imagine the top 40 hits you hear on the radio are songs written by robots rather than humans.

The possibilities are endless!

You might be wondering, "how is this course different from the first unsupervised deep learning course?"

In this first course, we still tried to learn the structure of data, but the reasons were different.

We wanted to learn the structure of data in order to improve supervised training, which we demonstrated was possible.

In this new course, we want to learn the structure of data in order to produce more stuff that resembles the original data.

This by itself is really cool, but we'll also be incorporating ideas from Bayesian Machine Learning, Reinforcement Learning, and Game Theory. That makes it even cooler!

Thanks for reading and I’ll see you in class. =)


"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

  • Probability

  • Object-oriented programming

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

  • Numpy coding: matrix and vector operations

  • Linear regression

  • Gradient descent

  • Know how to build a feedforward and convolutional 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:

  • Anyone who wants to improve their deep learning knowledge

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 40,634 Reviews
  • 148,359 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
  • 108,419 Reviews
  • 423,064 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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