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Build Basic Generative Adversarial Networks (GANs) に戻る による Build Basic Generative Adversarial Networks (GANs) の受講者のレビューおよびフィードバック



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....



The course provides good insight into the world of GANs. I really enjoyed Sharon's explanations which were deep and easy to understand. I really recommend this course to anyone interested in AI.


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.


Build Basic Generative Adversarial Networks (GANs): 51 - 75 / 344 レビュー

by Ernest W


This course is great, it presents GANs in an understandable way. The way how things are explained in each video gives a good delivery that encourages to further pursue the topic. Additional resources are included for more advanced explanations. Before choosing to start the course I've read some comments that it's too basic, maybe assignments are simple but it's not a course for someone with computer science or AI degree.

by Kulunu O


A Concise introduction to GANs! A good balance between theoretical explanations and practical implementation. Helped a lot to reach learning outcomes swiftly. Interactive jupyter notebooks are a great tool to familiarize on putting everything to work. The citations and links to respective research papers is a good approach to introduce the research practices to the pupils. Thank you for passing on the knowledge!

by Abishek B


The course was great and the slack community too. One issue was, some important topics were not introduced (vaguely introduced) in the video lectures and were asked to implement in the notebooks. Mainly, in Week 4 (for eg: regularization part). Also, the notebooks had more prewritten helping code.

by Rabin A


I found this course well paced and interesting. I didn't lose any interest in the course at any point at all. Although I only knew Tensorflow and Keras when starting the course, I was able to catch up with Pytorch framework. I recommend this course to everyone interested in GANs.

by Mayank A


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


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


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 1


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


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


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


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


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


A​wesome 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


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.



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


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


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


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


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

by Andrea Z


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


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


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


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, has been really one of the best places to learn.

by James C N


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


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.