Machine Learning- Generative Adversarial Networks (GANS)

Machine Learning: Generative Adversarial Networks (GANS), available at $49.99, has an average rating of 4.42, with 12 lectures, 1 quizzes, based on 6 reviews, and has 58 subscribers.

You will learn about Opportunities offered by generative models How GANs work (intuitive & mathematical) Implementing a GAN from scratch Synthetic image generation with GANs Dive in the paper Generative Adversarial Networks Implementation and optimization of neural networks with PyTorch This course is ideal for individuals who are Professionals who want to use the opportunities offered by generative models or Anyone interested in generative models and GANs or Anyone interested in machine learning & artificial intelligence or Anyone who would like to learn PyTorch through practise It is particularly useful for Professionals who want to use the opportunities offered by generative models or Anyone interested in generative models and GANs or Anyone interested in machine learning & artificial intelligence or Anyone who would like to learn PyTorch through practise.

Enroll now: Machine Learning: Generative Adversarial Networks (GANS)

Summary

Title: Machine Learning: Generative Adversarial Networks (GANS)

Price: $49.99

Average Rating: 4.42

Number of Lectures: 12

Number of Quizzes: 1

Number of Published Lectures: 12

Number of Published Quizzes: 1

Number of Curriculum Items: 13

Number of Published Curriculum Objects: 13

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Opportunities offered by generative models

How GANs work (intuitive & mathematical)

Implementing a GAN from scratch

Synthetic image generation with GANs

Dive in the paper Generative Adversarial Networks

Implementation and optimization of neural networks with PyTorch

Who Should Attend

Professionals who want to use the opportunities offered by generative models

Anyone interested in generative models and GANs

Anyone interested in machine learning & artificial intelligence

Anyone who would like to learn PyTorch through practise

Target Audiences

Professionals who want to use the opportunities offered by generative models

Anyone interested in generative models and GANs

Anyone interested in machine learning & artificial intelligence

Anyone who would like to learn PyTorch through practise

In this crash course, we will discuss the opportunities that generative models offer, and more specifically Generative Adversarial Networks (GANs).

I will explain how GANs work intuitively, and then we will dive into the paper that introduced them in 2014 (Ian J. Goodfellow et al.). You will therefore understand how they work in a mathematical way, which will give you the foundation to implement your first GAN from scratch.

We will implement in approximately 100 lines of code a generator, a discriminator and the pseudo-code described in the paper in order to train them. We will use the Python programming language and the PyTorch framework. After training, the generator will allow us to generate synthetic images that are indistinguishable from real images.

I believe that a concept is learned by doing and this crash course aims to give you the necessary basis to continue your learning of Machine Learning, PyTorch and generative models (GANS, Variational Autoencoders, Normalizing Flows, Diffusion Models, ).

At the end of this course, the participant will be able to use Python (and more particularly the PyTorch framework) to implement scientific papers and artificial intelligence solutions. This course is also intended to be a stepping stone in your learning of generative models.

Beyond GANs, this course is also a general introduction to the PyTorch framework and an intermediate level Machine learning course .

Concepts covered:

The PyTorch framework in order to implement and optimize neural networks.

The use of generative models in the research and industrial world.

GANs in an intuitive way. GANs in a mathematical way.

The generation of synthetic data (such as images).

The implementation of a scientific paper.

Don’t wait any longer before jumping into the world of generative models!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Applications

Chapter 2: GANs: explanation

Lecture 1: Intuitive explanation

Lecture 2: Mathematical explanation

Chapter 3: Implementation

Lecture 1: Google Colab

Lecture 2: Helpers

Lecture 3: Generator

Lecture 4: Discriminator

Lecture 5: Training loop

Lecture 6: Training

Lecture 7: Results

Chapter 4: Conclusion

Lecture 1: Conclusion

Instructors

Maxime Vandegar
Ingénieur de recherche

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 1 votes

4 stars: 2 votes

5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

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