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Avis et commentaires pour d'étudiants pour Apply Generative Adversarial Networks (GANs) par deeplearning.ai

4.8
étoiles
397 évaluations
84 avis

À propos du cours

In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one 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....

Meilleurs avis

UD
5 déc. 2020

I really liked the exposure to preparing various loss functions in paired and non-paired GANs, introduction to other applications, and many great changes to improve the quality of the networks!

MM
23 janv. 2021

GANs are awesome, solving many real-world problems. Especially unsupervised things are cool. Instructors are great and to the point regarding theoretical and practical aspects. Thankyou!

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76 - 85 sur 85 Avis pour Apply Generative Adversarial Networks (GANs)

par Stanislav K

31 janv. 2021

The course material is of very good quality. On the other hand, most of the coding exercises are limited to implementation of the loss functions. They are not teaching the students how to design the GAN architectures yourself.

par Rishab K

22 juin 2021

Very good course, assignment could be made more longer than what is currently here. Should also include a project at the end to implement GAN

par Aditya S

6 oct. 2021

Great course by a great instructor and great team behind! Learned sooooo damn much. Can't wait to go out and apply some of this stuff!

par Artod d

8 mars 2021

Not very well structured course. I think there is some room for improvements.

par Ibrahim G

3 nov. 2020

The assignments can go more in depth, but the content was great!

par Keebeom Y

16 nov. 2021

For English subtitles, there are many typos and sync of video and subtitles don’t match in some parts. Lecturer speaks too fast. But the content was very good, specifically coding projects.

par Mark P

15 nov. 2020

The programming assignments are too easy. Although the linked papers were useful I felt the optional notebooks should have been compulsory or we should have had to do more ourselves.

par Sameer R

22 oct. 2021

Too much repetition. More technical aspects could have been covered, given this is third course.

par Liang Y

29 mars 2021

The Instructor did a great job on scripts and PPTs. However, Instead of teaching you GANs, she reads the scripts in a super fast speed. It is good that if you are reporting or interviewing since your audiences are professors or specialists who are already very familiar with GANs. But I think most of the audiences here know little about GANs. I prefer Andrew Ng's teaching style which guides the audiences and gives them time to think and learn.

par Farhad D

15 nov. 2020

Exercises were so bad. They are very easy, and they are ambiguous a little bit. It seems the creators got tired at the end and they did a bad job. However, I learned a lot and I am thankful, but It could be much better!