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
1,246 ratings
304 reviews

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

MS
Oct 10, 2020

great course, only teaching what's needed, doesn't push you a lot in the coding assignments, as much as it requires you much more work to understand the codes and the science behind it.

DP
Oct 6, 2020

Excellent course. The videos were a pleasure to watch, the assignments were clear and allowed you to go as shallow or as in depth as you desired, and the mentors were very helpful.

Filter by:

51 - 75 of 314 Reviews for Build Basic Generative Adversarial Networks (GANs)

By Mayank A

Nov 27, 2020

I am really glad that I learned this Magical topic GANS. Thanks to all the mentors who taught this difficult topics with great ease and also to those mentors who promptly reply in the forum. Highly appreciate the Coursera community for spreading the knowledge across the world.

By Jaekoo K

Jan 3, 2021

I very much enjoyed this course. There are three points that I want to point out about this course:

1) The lecture is simple, but well organized.

2) The code examples/assignments are simple, but provoking more thoughts.

3) The Slack channel is really useful when you struggle.

By Mohan N

Oct 30, 2020

Sharon Zhou is a great instructor and manages to keep the flow of ideas always understandable and engaging. The assignments are also perfectly crafted with helpful unit tests to make the learning experience unhindered by confusing hiccups. This is the perfect way to learn.

By Alif A

Jan 15, 2021

As a beginner to GANs, this course offers a lot of new insights that I never came across before. It helped me understand a lot of the key terms used in current state of the art research papers and helped me understand a lot of the underlying working principles of GANs.

By Dai Q T

Dec 27, 2020

Thank you so much for providing this wonderful course. I've learned a lot from your wonderful lectures. Specifically, I really like the way you give your lecture, very concise and interesting. Thanks again, and hopefully a lot of people can enjoy the course as well.

By Earl W

Jan 10, 2021

The inclusion of unit tests and hints in the programming assignments are a huge "step up" from previous Coursera programming assignments. All Coursera classes should have used this model from the very beginning. Having said that, it's better late than never.

By Roee S

Jan 23, 2021

Excellent course. Explained in a very basic & understandable way for those who don't want to be complicated with too much mathematical background and still refers the participants to optional reading materials + active discussion in the course forums.

By Jiying L

Dec 6, 2020

Well designed exercise, in which I only need to read thru and understand the key points, and the actual coding part is very minimum. Courses are well taught with enough readings and reference provided. Most of them are up to date in research frontier.

By Mikiyas Z

May 23, 2021

Thank you all coursera, DeepLearning.AI, Slack Community Members. I get so many important knowledge and insights that will help to do my MSc. Thesis. my little suggestion is some module need more explanation for ML beginners. Thank You Again.

By DHRUV M

Jan 25, 2021

This course was awesome. All the concepts were up to point and all the detailed reading materials are provided. The notebook's configuration was perfect to train the model. Looking forward to the same experience in the next course.

By Khushwanth K R

Apr 1, 2021

Great explanation and great way to summarize huge topics but the assignments are really taking a huge time for training purpose if possible try to reduce the no.of epochs or provide a pre trained model and training the last layer

By Akhil K

Nov 6, 2020

The course is very good.The video lectures were super cool to understand.I just felt that the assignments should be a little bit more difficult like it should be given to write most of the code rather than filling just some cells

By Anantharaman N

Dec 10, 2020

Thanks much for the course. The contents are concise and optional material is called out separately. The speaker can slow down a bit as it's hard to keep pace understanding what she is saying and looking at the video contents.

By Roman V

Oct 24, 2020

Even though the lectures style is quite different from the previous deeplearning.ai courses (showing slides instead of explaining on a white-board), the Colabs made the understanding the concepts very visual and intuitive.

By Andrea Z

Nov 6, 2020

Very nice and informative introduction, even though it might be a bit difficult at times if you have never heard of concepts like "latent space" or "disentanglement" before :)

In all, really great work, thanks for this.

By Juan P J A

Oct 25, 2020

This course introduces concepts in a clear and simplified fashion and allows to have a hands on with basic GANs models. Further insight can be obtained through the recommended papers. I look forward to the next course

By lonnie

Apr 25, 2021

This is one of the most amazing and practical Deep Learning courses I have ever taken. This course dive deep into GAN and provide many notebooks and research papers for us to practice and explore. Thank you, Sharon.

By Devavrat S B

Nov 1, 2020

I have been trying to understand and implement GANs for que a few weeks and it felt really hard but after this course made everything easy for me, deeplearning.ai has been really one of the best places to learn.

By James C N

Nov 24, 2020

It would be helpful to include the formula of the normalization in the last parts of the assignment, as reading through the instruction is fine but having the actual regularization formula available is helpful.

By April P M M

Jun 21, 2021

T​his course is a truly amazing course. It bridges theory and practice and makes GAN easier to understand. You can also learn neat implementation of GANs that follows best software engineering practices.

By César A S C

Jan 18, 2021

Wonderful course for anyone interested in getting an introduction to GANs. All this knowledge will help me get closer to do research on state-of-the-art GAN models. Thank you for creating this material.

By Sebastian P

Oct 22, 2020

Excellent course, the first good thing is using PyTorch, love it , never had work with this framework and its really nice, second thing is about GANs, amazing topic I really want to learn more about it!

By André L B V

Mar 4, 2021

Great introduction of GANs. I particularly liked the programming assignments' difficulty (not too easy and not too hard). Also, the instructor is usually very clear and didactic.

By Sinan Ç

Oct 20, 2020

Excellent introduction to Generative Adversarial Networks (GANs). The course is easy to follow, and the assignments are challenging. Thanks for the great learning opportunity.

By Даниил Д Л

Mar 28, 2021

Very nice course to start your acquaintance with GANs. Loved the non-obvious mention of ProteinGANs to generate protein structures.

Definetly a recommendation for the novice.