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學生對 提供的 Build Better Generative Adversarial Networks (GANs) 的評價和反饋

248 個評分
34 條評論


In this course, you will: - Assess the challenges of evaluating GANs and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and the ways to detect it in GANs - Learn and implement the techniques associated with the state-of-the-art StyleGANs 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....



Assignments for me where the key to have proper understanding. The assignments are great where the difficulty gradually increases.


Week2 is little diverged, but concise detailed understanding explanation of style GAN is excellent. It is really worth.


1 - Build Better Generative Adversarial Networks (GANs) 的 25 個評論(共 35 個)

創建者 Dan I


Worse than the first course - 3 weeks of short and high level content, gains not applicable to the outside of the course. Covering StyleGAN as an advanced architecture does not lift up the shallowness of the course in my opinion. If you are considering to enroll, I recommend waiting until the last course is released because if the first two is an indication, you can easily finish all 3 courses within the 1 week trial period and get a free specialization certification.

創建者 Vitaly B


Too much attention to bias and fairness, but 3rd week is super cool!

創建者 Dmitry F


Not enough useful materal. A whole week (!) devoted to talking about bias, "protected classes" and other social issues as the authors see them... Create a separate course for those who are interested. You don't need to force-feed your religious beliefs, this is plain disrespectful.

創建者 Behnaz B


Big disappointment! This is not a course it is a seminar, it gives you some idea about some topics in GANs, leave you with a tones of papers to explore while giving little and vague explanations about almost every topic. Intuitive is not equal to unclear!

創建者 manohar2000


Week2 is little diverged, but concise detailed understanding explanation of style GAN is excellent. It is really worth.

創建者 Aniket M


Greate course content and assignments but I want to give one feedback to the instructor. Please keep some pause while speaking. She speaks way too fast.

創建者 K W


There are too many important building blocks that aren't really being fully explained. The programming assignments should have more, small code chunks for you to complete. I don't want to just fill in the last line of code after the first 200 lines have been written for you. I would suggest following Andrew's MOOC lecture format, with many short lectures, rather than a few long lectures. I would to understand each little topic really well, not get hit with everything at once. I would prefer to have the tests at the end of each topic like Andrew's Deep Learning specialization. DeepGAN wasn't really covered at all. I was a little disappointed with the final lab, after seeing what Style-GAN can do the entire course.

創建者 Akit M


Not enough content for an Andrew Ng course

創建者 Aladdin P


I've just completed the specialization and my thoughts are that everyone should take it (that are interested in GANs! I feel Sharon is a great teacher and the entire team did a really good job on putting togethor these courses. After completing it I definitely have a much better view of GANs, their architectures, successes and limitations, and have a solid background to tackle reading papers and implementing them on my own. Thank you for making this specialization!

With all the positives (which is why I rate it 5/5) there are in my opinion things that can be improved. Especially I think there is too much hand holding for the labs, out of 100 rows of codes I code maybe 2-3%. Many of these don't give much value coding but I want to feel like I did it! Unfortunately now I am left guessing if I have truly mastered the material (and I'm quite sure I haven't, so I will need to re-implement these on my own). Also since you state that calculus and linear algebra are prerequisites then stick with it! You are trying to be too inclusive and there are several part of the courses where I thought it was entirely unecessary because everyone taken Calc and Linalg already has this knowledge. I would prefer instead if you spend this time making other videos where you go in more depth, perhaps going through some of the difficult math etc. Hopefully you try to improve this for future courses done by

創建者 Matthew B E R


Another beautifully clear course. In particular I enjoyed FID and found A Survey on Bias and Fairness in Machine Learning an interesting read. The assignments, as always, are enjoyable, and through the unit testing give a more practical understanding of what is going on.

創建者 German E G J


Very good course! Helpful to understand evaluation metrics and details of Style GAN. It was also super cool to have the bias section that is not as well known as the others. Loved it!

創建者 Akshai S


Build state of the art models in a course is not an easy feat. Thanks to all the materials that have been provided.

創建者 munirs90


Name explains that it is better version than previous in terms of learning and study state of the art GANs

創建者 Csanád E


Excellent course! Great videos, somewhat challenging assignments, fantastic community.

創建者 maulik p


Nice explanation of state of the art StyleGAN architecture and advanced techniques

創建者 Khushpreet S


It was much needed, thank you for bringing GAN speacialization

創建者 Neeraj P


A very informative knowledge boosting course on how far GANs have come.

創建者 Olivier M


Amazing lectures on very complex topics. Thanks a lot

創建者 Shivender K


Highly complex and interesting course to build GAN knowledge.

創建者 Jorge P


Excelente contenido, me encantan las actividades.

創建者 Gokulakannan S


Nice course. Enjoyed every bit of it@

創建者 Nancy A A


Great clear course

創建者 Tim C


Great stuff ! :)

創建者 Jason C


Great stuff!

創建者 vignesh m