Introduction to Generative Adversarial Networks with PyTorch

Online Course

Udemy

What is the course about?
Prerequisites & Facts
Cost, Certificate & Degree
Reviews & Experiences

What is the course about?

Introduction to Generative Adversarial Networks with PyTorch

The course Introduction to Generative Adversarial Networks with PyTorch is an online class provided by Udemy. It may be possible to receive a verified certification or use the course to prepare for a degree.

A comprehensive course on GANs including state of the art methods, recent techniques, and step-by-step hands-on projects

Course description

Introduction to Generative Adversarial Networks with PyTorch

How Generative Adversarial Networks work internally
How to implement state of the art GANs techniques and methods using PyTorch
How to improve the training stability of GANs

Familiarity with Python Programming
Familiarity with Deep Learning Concepts

6 hours on-demand video
6 downloadable resources
Full lifetime access
Access on mobile and TV
Certificate of Completion
Mustafa Qamaruddin

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Prerequisites & Facts

Introduction to Generative Adversarial Networks with PyTorch

Course Topic

Development, ECMAscript, Web Development

University, College, Institution

Udemy

Course Skill Level

see course summary

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

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Degree & Cost

Introduction to Generative Adversarial Networks with PyTorch

To obtain a verified certificate from Udemy you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.

Udemy

Introduction to Generative Adversarial Networks with PyTorch

provided by Udemy

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School: Udemy

Topic: Data Science, Development, Generative Adversarial Networks (GAN)

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