Generative Adversarial Networks (GAN): The Complete Guide

Generative Adversarial Networks (GAN): The Complete Guide, available at $39.99, has an average rating of 4.7, with 20 lectures, based on 28 reviews, and has 154 subscribers.

You will learn about Learn the basic principles of generative models Build a GAN (Generative Adversarial Network) in Tensorflow Tensorflow DCGAN WGAN This course is ideal for individuals who are Anyone who wants to improve their deep learning knowledge It is particularly useful for Anyone who wants to improve their deep learning knowledge.

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Summary

Title: Generative Adversarial Networks (GAN): The Complete Guide

Price: $39.99

Average Rating: 4.7

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Anyone who wants to improve their deep learning knowledge

Target Audiences

Anyone who wants to improve their deep learning knowledge

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…

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Structure

Lecture 2: How to make the course out of this course

Lecture 3: Introduction to GAN

Lecture 4: GAN Project: Importing libraries and data

Lecture 5: GAN Project: Generator Construction

Lecture 6: GAN Project: Discriminator Construction

Lecture 7: GAN Project: Defining optimizer and loss

Lecture 8: GAN Project: Fully Connected Network and results

Chapter 2: Introduction to DCGAN

Lecture 1: Introduction to DCGAN and project implementation Part 1

Lecture 2: Introduction to DCGAN and project implementation Part 2

Lecture 3: Introduction to DCGAN and project implementation Part 3

Lecture 4: Introduction to DCGAN and project implementation Part 4

Lecture 5: Introduction to DCGAN and project implementation Final Part

Chapter 3: WGAN

Lecture 1: Introduction to WGAN

Lecture 2: WGAN Implementation Part 1

Lecture 3: WGAN Implementation Part 2

Lecture 4: WGAN Implementation Part 3

Lecture 5: WGAN Implementation Part 4

Lecture 6: WGAN Implementation final Part

Chapter 4: Thank you

Lecture 1: Thank you

Instructors

Hoang Quy La
Electrical Engineer

Rating Distribution

1 stars: 5 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 0 votes
5 stars: 22 votes

Frequently Asked Questions

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