Deep Learning: GANs and Variational Autoencoders

Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow

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Course Data

Lectures: 55
Length: 7h 48m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course 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. =)



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 and TensorFlow


Tips for success:

  • Use the video speed changer! Personally, I like to watch at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
  • Write code yourself, this is an applied course! Don't be a "couch potato".

Testimonials and Success Stories


I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

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I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

Lectures

Introduction and Outline

5 Lectures · 23min
  1. Welcome (04:37) (FREE preview available)
  2. Where does this course fit into your deep learning studies? (05:00)
  3. Where to get the code and data (03:52)
  4. How to succeed in this course (05:52)
  5. Tensorflow or Theano - Your Choice! (04:10)

Generative Modeling Review

9 Lectures · 54min
  1. What does it mean to Sample? (04:58)
  2. Sampling Demo: Bayes Classifier (03:58)
  3. Gaussian Mixture Model Review (10:32)
  4. Sampling Demo: Bayes Classifier with GMM (03:54)
  5. Why do we care about generating samples? (11:20)
  6. Neural Network and Autoencoder Review (07:26)
  7. Tensorflow Warmup (04:08)
  8. Theano Warmup (04:54)
  9. Suggestion Box (03:03)

Variational Autoencoders

13 Lectures · 01hr 25min
  1. Variational Autoencoders Section Introduction (05:39)
  2. Variational Autoencoder Architecture (05:57)
  3. Parameterizing a Gaussian with a Neural Network (08:00)
  4. The Latent Space, Predictive Distributions and Samples (05:13)
  5. Cost Function (07:29)
  6. Tensorflow Implementation (pt 1) (07:19)
  7. Tensorflow Implementation (pt 2) (02:30)
  8. Tensorflow Implementation (pt 3) (09:55)
  9. The Reparameterization Trick (05:06)
  10. Theano Implementation (10:52)
  11. Visualizing the Latent Space (03:09)
  12. Bayesian Perspective (10:11)
  13. Variational Autoencoder Section Summary (04:03)

Generative Adversarial Networks (GANs)

11 Lectures · 01hr 51min
  1. GAN - Basic Principles (05:14)
  2. GAN Cost Function (pt 1) (07:24)
  3. GAN Cost Function (pt 2) (06:28)
  4. DCGAN (07:38)
  5. Batch Normalization Review (08:02)
  6. Fractionally-Strided Convolution (08:35)
  7. Tensorflow Implementation Notes (13:24)
  8. Tensorflow Implementation (18:13)
  9. Theano Implementation Notes (07:26)
  10. Theano Implementation (19:47)
  11. GAN Summary (09:44)

Theano and Tensorflow Review

4 Lectures · 34min
  1. Theano Basics: Variables, Functions, Expressions, Optimization (07:47)
  2. Building a neural network in Theano (09:17)
  3. TensorFlow Basics: Variables, Functions, Expressions, Optimization (07:27)
  4. Building a neural network in TensorFlow (09:43)

Setting Up Your Environment (Appendix/FAQ by Student Request)

2 Lectures · 37min
  1. Windows-Focused Environment Setup 2018 (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

5 Lectures · 52min
  1. How to Code Yourself (part 1) (15:55)
  2. How to Code Yourself (part 2) (09:24)
  3. Proof that using Jupyter Notebook is the same as not using it (12:29)
  4. Python 2 vs Python 3 (04:38)
  5. Is Theano Dead? (10:04)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)

Extras

  • GAN Tutorial PDF
  • Variational Autoencoder Tutorial PDF
  • Pre-trained Style Transfer Network Ready for Use
  • GAN in Tensorflow 2
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