Build a successful career in Artificial Intelligence
  1. Great Learning
  2. Free Courses
  3. Artificial Intelligence

Free GAN Course with Certificate

Generative Adversarial Networks

Enrol for this free introductory course on Generative Adversarial Networks (GANs) to expand your expertise in this field. Benefit from the guidance of our seasoned instructors. Begin your learning journey today!

4.45
average rating

Ratings

Beginner

Level

1.5 Hrs

Learning hours

1.1K+

Learners

Skills you’ll Learn

About this Free Certificate Course

This comprehensive course on Generative Adversarial Networks (GANs) equips you with the knowledge and skills to master the art of creating artificial intelligence-driven data generators. Designed for both beginners and experienced practitioners, this course covers a wide spectrum of topics. You'll begin with the basics, delving into deep learning, neural networks, and the fundamentals of generative models. Building on this foundation, you'll explore the core concepts of GANs, including their architecture and components. As you progress, you'll advance to more complex subjects, such as advanced GAN architectures, practical tools, and real-world applications. Discover the challenges and limitations GANs face and explore the latest advancements in this dynamic field.

 

Practical hands-on experience is emphasized throughout the course, ensuring you can confidently apply GAN techniques in various domains. By the end, you'll have a deep understanding of GANs, their applications, and the skills to undertake exciting AI projects. Whether you're looking to enhance your AI knowledge or embark on a career in artificial intelligence, this course provides the expertise needed to harness the creative power of Generative Adversarial Networks.

Why upskill with us?

check circle outline
1000+ free courses
In-demand skills & tools
access time
Free life time Access

Course Outline

Introduction to Deep Learning and Neural Networks

 Learn the fundamentals of neural networks and their role in deep learning.

Understanding Convolutional Neural Networks (CNNs)

Explore the architecture and applications of CNNs for image-related tasks.

Basic Concepts and Terminology

Get acquainted with essential terminology and concepts in the context of generative adversarial networks (GANs).

Introduction to Generative Models

Discover the basics of generative models and their significance in creating new data.

Foundations of Generative Adversarial Networks (GANs)

Build a strong foundation in GANs, their components, and how they generate data.

Advanced GAN Architectures and Techniques

Dive into advanced GAN variations and cutting-edge techniques for improved performance.

Getting Started with GAN: Tools and Resources

Get hands-on experience with GANs using essential tools and resources.

GAN Applications and Use Cases

Explore practical applications and real-world use cases of GAN technology.

Challenges and Limitations of GANs

Understand the inherent challenges and limitations GANs face in various scenarios.

Recent Advancements and Future Directions in GAN Research

Stay up-to-date with the latest developments and future prospects in GAN research.

Tips and Best Practices for Working with GANs

Learn valuable tips and best practices for effectively working with GANs.

Project Ideas

Generate creative project ideas to apply your GAN knowledge and skills in real-world scenarios.

Premium programs from top universities

Make the right decision for your career growth today!

KNOW MORE

Trusted by 1 Crore+ Learners globally

What our learners say about the course

Find out how our platform helped our learners to upskill in their career.

4.45
Course Rating
71%
15%
10%
2%
2%

Ratings & Reviews of this Course

Reviewer Profile
PRAJYOT DAPHAL

5.0

“Exceptional Introduction to GANs Course!”
This course on Generative Adversarial Networks (GANs) is truly exceptional! It provides clear and informative content that simplifies complex concepts. I appreciated how the course builds a strong foundation, making it accessible for beginners while still engaging for those with some background knowledge. Highly recommend it to anyone interested in diving into the world of generative models!
linkedin profile

Generative Adversarial Networks

1.5 Learning Hours . Beginner

Why upskill with us?

check circle outline
1000+ free courses
In-demand skills & tools
access time
Free life time Access

Frequently Asked Questions

What are the prerequisites required to learn this Free Generative Adversarial Networks Course?

You do not need any prior knowledge to learn this Generative Adversarial Networks Course. 

How long does it take to complete this Free Generative Adversarial Networks Course?

It is a 1.0-hour long course, but it is self-paced. Once you enrol, you can take your own time to complete the course.

Will I have lifetime access to the free course?

Yes, once you enrol in the course, you will have lifetime access to any of the Great Learning Academy’s free courses. You can log in and learn whenever you want to.

Will I get a certificate after completing this Free Generative Adversarial Networks Course?

Yes, you will get a certificate of completion after completing all the modules and cracking the assessment. 

How much does this Generative Adversarial Networks Course cost?

It is an entirely free course from Great Learning Academy.

Is there any limit on how many times I can take this free course?

No. There is no limit.

Recommended Free AI courses

Free
AI in Healthcare
course card image

Free

Beginner

Free
Neural Network in R
course card image

Free

INTERMEDIATE

Free
AI Ethics for Beginners
course card image

Free

Beginner

Free
Introduction to Transfer Learning
course card image

Free

Beginner

Similar courses you might like

Free
Spyder Python
course card image

Free

Beginner

Free
Introduction to Artificial Intelligence
course card image

Free

Beginner

Free
Application of Machine Learning in Finance
course card image

Free

Beginner

Free
NLP Customer Experience
course card image

Free

INTERMEDIATE

Related Artificial Intelligence Courses

50% Average salary hike
Explore degree and certificate programs from world-class universities that take your career forward.
Programs starting at ₹5,000 per month
checkmark icon
Placement assistance
checkmark icon
Personalized mentorship
checkmark icon
Detailed curriculum
checkmark icon
Learn from world-class faculties

Other Artificial Intelligence tutorials for you

Generative Adversarial Networks

Generative Adversarial Networks (GANs) stand as one of the most revolutionary developments in the field of artificial intelligence, specifically in the domain of generative modeling. Conceived by Ian Goodfellow and his colleagues in 2014, GANs have spurred numerous advancements in generating realistic and coherent data, impacting domains like image synthesis, style transfer, text-to-image generation, and more.

At the heart of a GAN is a novel approach that involves two neural networks pitted against each other in a strategic contest, hence the name "adversarial." The two components are the generator and the discriminator. The generator's role is to fabricate data instances, such as images or text, that are intended to resemble authentic examples from a given dataset. In contrast, the discriminator's task is to differentiate between genuine data and those generated by the generator.

The process begins with the generator crafting a synthetic data instance. During the initial stages, these creations are typically random and far from convincing. The discriminator then evaluates these samples, attempting to classify them correctly as either real or fake. As training progresses, the generator refines its output in an attempt to deceive the discriminator into wrongly labeling its creations as real.

Concurrently, the discriminator becomes more adept at distinguishing real from generated instances. It adjusts its parameters to minimize its classification errors. The iterative tug-of-war between the two networks propels both towards improvement, with the generator striving to produce increasingly authentic outputs and the discriminator becoming more discerning.

This intricate interplay eventually leads to a point where the generator's outputs are indistinguishable from real data to the discriminator, resulting in a successful GAN. The key breakthrough lies in the fact that the generator learns to capture the underlying distribution of the training data, effectively grasping the essence of the dataset and producing instances that possess remarkably similar characteristics.

GANs have found extensive applications in various domains. In image generation, they have produced astonishingly realistic artwork, deepfake videos, and even aided in medical image synthesis. Style transfer, where the artistic style of one image is applied to another, has been significantly enhanced by GANs. Text-to-image synthesis, a particularly challenging task, has seen progress through GANs by learning the intricate relationship between textual descriptions and corresponding images.

However, GANs come with their own set of challenges. The training process can be delicate, often requiring careful tuning of hyperparameters to ensure stable convergence. Mode collapse, a phenomenon where the generator fixates on producing only a limited variety of outputs, is another hurdle. Researchers have devised techniques such as Wasserstein GANs and progressive training to address these issues, making GANs more reliable and effective.

Looking ahead, the evolution of GANs continues. Progressive GANs have enabled the generation of high-resolution images, while conditional GANs allow for targeted generation based on specific input conditions. Attention has also shifted towards ethical considerations, given GANs' potential to generate misinformation and deepfakes.

In summary, Generative Adversarial Networks have revolutionized the landscape of generative modeling. Their innovative adversarial approach has led to impressive advancements in image synthesis, style transfer, text-to-image conversion, and more. While challenges persist, the potential applications and ongoing research in the field of GANs promise to reshape various industries and push the boundaries of what is possible in artificial intelligence and creative expression.
 

Enrol for Free

Refer & Win >

Premium course worth ₹15,000/-