Deep Learning: GANs and Variational Autoencoders

Deep Learning: GANs and Variational Autoencoders

5.5 Hours
Deal Price$25.00
Suggested Price
$180.00
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Deep Learning: GANs and Variational Autoencoders
$25.00$180.0086% OFF
Deep Learning: GANs and Variational Autoencoders

41 Lessons (5.5h)

  • Introduction and Outline
    Welcome4:33
    Where does this course fit into your deep learning studies?5:00
    Where to get the code and data3:51
    How to succeed in this course5:19
  • Generative Modeling Review
    What does it mean to Sample?4:57
    Sampling Demo: Bayes Classifier3:57
    Gaussian Mixture Model Review10:31
    Sampling Demo: Bayes Classifier with GMM3:54
    Why do we care about generating samples?
    Neural Network and Autoencoder Review7:26
    Tensorflow Warmup4:07
    Theano Warmup4:54
  • Variational Autoencoders
    Variational Autoencoders Section Introduction5:39
    Variational Autoencoder Architecture5:57
    Parameterizing a Gaussian with a Neural Network8:00
    The Latent Space, Predictive Distributions and Samples5:13
    Cost Function7:28
    Tensorflow Implementation (pt 1)7:18
    Tensorflow Implementation (pt 2)2:29
    Tensorflow Implementation (pt 3)9:55
    The Reparameterization Trick5:05
    Theano Implementation10:52
    Visualizing the Latent Space3:09
    Bayesian Perspective3:09
    Variational Autoencoder Section Summary4:02
  • Generative Adversarial Networks (GANs)
    GAN - Basic Principles5:13
    GAN Cost Function (pt 1)7:23
    GAN Cost Function (pt 2)4:56
    DCGAN7:38
    Batch Normalization Review8:01
    Fractionally-Strided Convolution8:35
    Tensorflow Implementation Notes13:23
    Tensorflow Implementation18:13
    Theano Implementation Notes7:26
    Theano Implementation19:47
    GAN Summary9:43
  • Appendix
    How to How to install Numpy, Theano, Tensorflow, etc...17:32
    How to Succeed in this Course (Long Version)5:55
    How to Code by Yourself (part 1)15:54
    How to Code by Yourself (part 2)9:23
    Where to get discount coupons and FREE deep learning material2:20
Deep Learning: GANs and Variational Autoencoders
$25.00$180.0086% OFF
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Generative Adversarial Networks & Variational Autoencoders in Python, Theano, & Tensorflow

LP
Lazy ProgrammerThe Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons to communicate with their family and caregivers.

He has worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. He has created new big data pipelines using Hadoop/Pig/MapReduce, and created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.

He has 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, Humber College, and The New School.

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

Description

Variational autoencoders and GANs have been two of the most interesting recent developments in deep learning and machine learning. GAN stands for generative adversarial network, where two neural networks compete with each other. Unsupervised learning means you're not trying to map input data to targets, you're just trying to learn the structure of that input data. In this course, you'll learn the structure of data in order to produce more stuff that resembles the original data.

  • Access 41 lectures & 5.5 hours of content 24/7
  • Incorporate ideas from Bayesian Machine Learning, Reinforcement Learning, & Game Theory
  • Discuss variational autoencoder architecture
  • Discover GAN basics

Specs

Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels, but knowledge of calculus, probability, object-oriented programming, Python, Numpy, linear regression, gradient descent, and how to build a feedforward and convolutional neural network in Theano and TensorFlow is expected
  • All code for this course is available for download here, in the directory unsupervised_class3

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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