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

4.7
stars
1,913 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.

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76 - 100 of 446 Reviews for Build Basic Generative Adversarial Networks (GANs)

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 Sudhakar M

Nov 21, 2021

Awesome Learning Experience. The topic itself is so much interesting and fun. With this course, not only I learned this amazing tool but also reminded about the sense of responsibility in using this tool. Thanks a lot for teaching the course.

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 Usama I B

Aug 27, 2022

This course helped me understand the foundations of GANs. It's full of coding assignments & references to original papers introducing different architectures. It is a best choice if you're absolution beginner to GANs.

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 Muhammad Z R

Feb 23, 2023

Nicely explains intuition behind things to implement. Walks through from most basic GAN to better versions and what improvements needed. Notebooks are good, even beginners in PyTorch can learn. Recommended.

By April P M M

Jun 21, 2021

This 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 Cesar S

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 Kulbir m

Jul 21, 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

By Hoan L

Mar 11, 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.

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 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.

By Wenhui L

Oct 14, 2020

The course is great with hands-on experiments. The assignments are properly designed to let the learner focus on the most important pieces of the logic in the implementation

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.