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Generative Adversarial Networks (GAN): The Complete Guide
Rating: 3.6 out of 5(42 ratings)
185 students

Generative Adversarial Networks (GAN): The Complete Guide

Generative Adversarial Networks in Python
Created byHoang Quy La
Last updated 3/2022
English

What you'll learn

  • Learn the basic principles of generative models
  • Build a GAN (Generative Adversarial Network) in Tensorflow
  • Tensorflow
  • DCGAN
  • WGAN

Course content

4 sections • 20 lectures • 3h 47m total length

  • Course Structure1:01

    Outline the course structure and costs, introduce yarn and code, and map progress through GAN topics with planned updates and future content.

  • How to make the course out of this course1:52
  • Introduction to GAN20:54
  • GAN Project: Importing libraries and data11:15
  • GAN Project: Generator Construction17:08

    In this lecture, we construct the generator for the GAN project, configuring nz, ngf, and nc, building a sequential convolutional generator and implementing its forward pass.

  • GAN Project: Discriminator Construction13:31

    Iterate on discriminator construction for GANs, copy and adapt code, compare activation choices such as leaky relu, and implement global average pooling to streamline the network.

  • GAN Project: Defining optimizer and loss4:59
  • GAN Project: Fully Connected Network and results17:56

    Train a fully connected network as a generative adversarial network, with a generator producing samples and a discriminator learning to distinguish real and fake images.

Requirements

  • Calculus
  • Probability
  • Object-oriented programming
  • Python coding: if/else, loops, lists, dicts, sets
  • Basic deep learning

Description

GANs have been one 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.


This course is a comprehensive guide to Generative Adversarial Networks (GANs). The theories are explained in-depth and in a friendly manner. After each theoretical lesson, we will dive together into a hands-on session, where we will be learning how to code different types of GANs in PyTorch and Tensorflow, which is a very advanced and powerful deep learning framework!

In this first course, You will learn

  • GAN

  • DCGAN

  • WGAN


"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...



Who this course is for:

  • Anyone who wants to improve their deep learning knowledge

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