Chevron Left
Back to Build Basic Generative Adversarial Networks (GANs)

Learner Reviews & Feedback for Build Basic Generative Adversarial Networks (GANs) by DeepLearning.AI

4.7
stars
1,946 ratings

About the Course

In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research....

Top reviews

KM

Jul 20, 2023

Helped me clarify the some of key principles and theories behind GAN and bit of history... The references/additional study materials are very useful, if you want to dig deep into. Overall very pleased

HL

Mar 10, 2022

Great introductory to GANs, focused on the building blocks to neural net/ GANs, and a bit of frequently used models. Might need a small update on what's considered "state-of-the-art" in the course.

Filter by:

101 - 125 of 449 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Ashish

•

Nov 1, 2020

Good overall introduction to GANs. I really liked how well the sections on Wasserstein Loss and Conditional & Controllable GAN sections were covered in this course.

By Hernandez M K J

•

Dec 10, 2020

This course was awesome. Concise, simple and straightforward. The course teaches something very sophisticated but the instructor made it very easy to understand.

By Rafael M

•

Jul 27, 2021

Awesome course. Like any other from DeepLearnin.AI, the content is given in a intuitive way, so that you can learn easily. Congratulations for the creators!

By Sebastian K

•

Nov 17, 2020

Great course! The programming assignments were a bit short and too easy. The Deep Learning Specialization assignments had the ideal difficulty and length.

By Bogdan K

•

Jun 10, 2024

Amazing course, one of the best I've ever enrolled in. The speaker, presentation, labs and provided resources are all very very good and well documented!

By Aayush R

•

Feb 10, 2024

Excellent Course to get started with GAN's. Can't wait to explore other parts of this specialization. Thank you Deeplearning.AI for this amazing content.

By Muhammad T W

•

Sep 8, 2023

Had a wonderful experience with this course, The instructer is teaching in an energetic way that you feel you are diving in the depth of GAN very easily

By Arvind K V

•

Oct 16, 2020

I really like the way he teaches all the concept from scratch. i learn a lot

any one want to learn foundation for GAN i really recommend them this course

By Lambertus d G

•

Jan 9, 2021

Sharon rocks! Very clear explanation of quite complicated material makes it relatively easy to understand GANs. Looking forward to starting course 2!

By Nastaran E

•

Nov 10, 2020

I really enjoyed taking this course on GANs. It walked me through the concepts in a reasonable speed and provided detailed explanations and insights.

By Rajib K C

•

May 13, 2022

It is a very nicely orgranized course that will provide a great understanding how GAN works and it's intuition with some hands on coding practices.

By Yoel S

•

Apr 10, 2021

Excellent

Well organized, clarifies terms and concepts, high implementation

quality of assignments, impressively up-to-date on new works (Apr 2021)

By Ryan C

•

Dec 13, 2021

This is such a great course. Explanation and guidance throughout the course was excellent. A huge thanks to our lecturer Sharon, Eric, and Eda.

By NIRMAL N

•

Jul 22, 2023

Amazing course, got to learn a lot. The assignments felt a lil difficult for someone not so well versed in python but was an informative one!

By Aditya A K

•

Dec 31, 2020

This course rightly covers the introduction of both Pytorch and GANs so that the natural interest for further courses keeps increasing.

By Rafael P

•

Nov 14, 2020

I loved it! The guided notebooks are great to make sure I am not doing any mistake and also providing unit tests in important cells.

By Shubhankar S

•

Nov 8, 2020

A really good course to learn about GANs, reading the quoted research papers will help develop a better intuition and understanding.

By Hashan A

•

Oct 11, 2020

Good job at explaining theories quickly. The assignments helped to learn pytorch and also to verify the understanding of principles.

By Zahid A

•

Jun 14, 2021

One of the Amazing course on the Coursera Platform. Due to these courses I had choose my Final Year Project on GAN. Happy learning.

By Vishnu N

•

Dec 12, 2020

Thank you Sharon Zhou and other Instructors for this interesting course on Generative Adversarial Networks (GANs)

ThankYou Coursera.

By Aleks S

•

Oct 21, 2020

Good course overall. I don't feel ready to implement GANs after the assignments though because so much of the code was pre-written.

By George N

•

Nov 18, 2020

So awesomely taught. Assignments were motivatingly easy and optional advanced material provided for those who want to delve deeper

By Bharath P

•

Oct 8, 2020

Nice Course. Lot of depth concepts were really simplified. Better to get good understanding of pytorch to follow Assignemnets well

By Akshai S

•

Jan 14, 2021

The course has meticulously designed for easier understanding. One has to complete the assignments to get hands on experience.

By Arkady A

•

Dec 26, 2020

Awesome course with clear and straightforward instruction - I felt motivated to complete this 4 week course in just two weeks.