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Отзывы учащихся о курсе Build Basic Generative Adversarial Networks (GANs) от партнера deeplearning.ai

4.7
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Оценки: 1,513

О курсе

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....

Лучшие рецензии

HL

10 мар. 2022 г.

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.

WM

1 окт. 2020 г.

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.

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51–75 из 372 отзывов о курсе Build Basic Generative Adversarial Networks (GANs)

автор: Ayan G

20 нояб. 2020 г.

Really amazing course (as expected from deeplearning.ai), I especially liked the detail description of almost everything in notebook assignments, Also the cool reference and advance topic. The simplified explanation of maths formula.

Also, I think infoGan paper and notebook should be moved after disentanglement video since these concept discussed in the paper are relevant to those videos.

Thank you for such an amazing course 🙂

автор: Ernest W

6 янв. 2022 г.

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.

автор: Kulunu O

9 янв. 2022 г.

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!

автор: Abishek B

6 янв. 2021 г.

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.

автор: Rabin A

22 окт. 2020 г.

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.

автор: Mayank A

27 нояб. 2020 г.

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.

автор: Jaekoo K

3 янв. 2021 г.

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.

автор: Mohan N

30 окт. 2020 г.

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.

автор: Alif A 1

15 янв. 2021 г.

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.

автор: Dai Q T

27 дек. 2020 г.

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.

автор: Earl W

10 янв. 2021 г.

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.

автор: Venkatesan K

5 апр. 2022 г.

Managed to learn the foundations of building GANs. The course is paced very well and the assignments are super interesting as well. Thank you Sharon, you are an amazing instructor! Can't wait to learn more advanced topics of GANs related to computer vision.

автор: Alex

30 апр. 2022 г.

I loved it. It was very tough at first and I definitely need to review the code as a whole after finishing, but I learned so much in the process. Some of the courses on Coursera are so simple that they are not worth the money, but this course is worth it.

автор: Roee S

23 янв. 2021 г.

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.

автор: Jiying L

6 дек. 2020 г.

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.

автор: Sudhakar M

21 нояб. 2021 г.

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.

автор: Mikiyas Z

23 мая 2021 г.

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.

автор: DHRUV M

25 янв. 2021 г.

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.

автор: Khushwanth K R

1 апр. 2021 г.

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

автор: Akhil K

6 нояб. 2020 г.

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

автор: Anantharaman N

10 дек. 2020 г.

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.

автор: Roman V

24 окт. 2020 г.

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.

автор: Andrea Z

6 нояб. 2020 г.

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.

автор: Juan P J A

25 окт. 2020 г.

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

автор: lonnie

25 апр. 2021 г.

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.