Learning GANs and Variational Autoencoders Free Video Course Free Download
GANs and Variational Autoencoders in Python, Theano, and Tensorflow
What you will 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
Requirements For This Course :
- Know how to build a neural network in Theano and/or Tensorflow
- Multivariate Calculus
- Numpy, etc.
Description Of GANs and Variational Autoencoders :
Variational autoencoders and GANs have been 2 of the most fascinating advancements with regards to profound learning and AI as of late.
Yann LeCun, a profound learning pioneer, has said that the most significant improvement lately has been ill-disposed preparing, alluding to GANs.
GAN represents generative ill-disposed system, where 2 neural systems rival one another.
What is unaided realizing?
Solo learning implies we’re doing whatever it takes not to plan input information to targets, we’re simply attempting to get familiar with the structure of that input information.
When we have discovered that structure, we can do some entirely cool things.
One model is producing verse – we’ve done instances of this previously.
In any case, verse is a quite certain thing, what about writing when all is said in done?
On the off chance that we can get familiar with the structure of language, we can create any sort of text. Actually, enormous organizations are placing in heaps of cash to explore how the news can be composed by machines.
Be that as it may, consider the possibility that we return to verse and remove the words.
Well then we get craftsmanship, by and large.
By learning the structure of craftsmanship, we can make more workmanship.
What about workmanship as sound?
In the event that we become familiar with the structure of music, we can make new music.
Envision the main 40 hits you hear on the radio are tunes composed by robots as opposed to people.
The potential outcomes are unfathomable!
You may be pondering, “how is this course unique in relation to the main solo profound adapting course?”
In this first course, we despite everything attempted to gain proficiency with the structure of information, however the reasons were extraordinary.
We needed to gain proficiency with the structure of information so as to improve administered preparing, which we showed was conceivable.
In this new course, we need to gain proficiency with the structure of information so as to create more stuff that takes after the first information.
This without anyone else is truly cool, however we will likewise be consolidating thoughts from Bayesian Machine Learning, Reinforcement Learning, and Game Theory. That makes it much cooler!
Much obliged for perusing and I will see you in class. =)
- Item situated programming
- Python coding: if/else, circles, records, dicts, sets
- Numpy coding: network and vector tasks
- Straight relapse
- Slope plummet
- Expertise to fabricate a feedforward and convolutional neural system in Theano or TensorFlow
- TIPS (for overcoming the course):
Watch it at 2x.
- Take written by hand notes. This will radically build your capacity to hold the data.
- Record the conditions. On the off chance that you don’t, I promise it will simply look like babble.
- Pose loads of inquiries on the conversation board. The more the better!
- Understand that most activities will take you days or weeks to finish.
- Compose code yourself, don’t simply stay there and take a gander at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Look at the talk “What request should I take your courses in?” (accessible in the Appendix of any of my courses, including the free Numpy course)
Who this course is for :
- Anyone who wants to improve their deep learning knowledge
Course Content Of GANs and Variational Autoencoders :
1. Introduction & Outline :
- Where to get the code and data
- How to succeed in this course
2. Generative Modeling review
- What does it mean to Sample?
- Sampling Demo: Bayes Classifier
- Gaussian Mixture Model Review
- Sampling Demo: Bayes Classifier with GMM
- Why do we care about generating samples?
- Neural Network and Autoencoder Review
- Tensorflow Warmup
- Theano Warmup
- Suggestion Box
3. Variational Autoencoders
- Variational Autoencoder Architecture
- Parameterizing a Gaussian with a Neural Network
- The Latent Space, Predictive Distributions and Samples
- Cost Function
- Tensorflow Implementation (pt 1)
- Tensorflow Implementation (pt 2)
- Tensorflow Implementation (pt 3)
- The Reparameterization Trick
- Theano Implementation
- Visualizing the Latent Space
- Bayesian Perspective
- Variational Autoencoder Section Summary
4. Generative Adversarial Networks (GANs)
- GAN – Basic Principles
- GAN Cost Function (pt 1)
- GAN Cost Function (pt 2)
- Batch Normalization Review
- Fractionally-Strided Convolution
- Tensorflow Implementation Notes
- Tensorflow Implementation
- Theano Implementation Notes
- Theano Implementation
- GAN Summary
5. Theano & Tensorflow Basics Review
- (Review) Theano Basics
- (Review) Theano Neural Network in Code
- (Review) Tensorflow Basics
- (Review) Tensorflow Neural Network in Cod
5. Setting Up Your Enviroment
- Windows-Focused Environment Setup 2018
- How to How to install Numpy, Theano, Tensorflow, etc…
6. Extra Help With Python Coding For Begineers
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
- Is Theano Dead?
7. Effective Learning Stratefies For Machine Learning
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- What order should I take your courses in? (part 1)
- What order should I take your courses in? (part 2)
- What is the Appendix?
- Where to get discount coupons
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