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

4.7
stars
1,111 ratings
277 reviews

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

MS
Oct 10, 2020

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.

DP
Oct 6, 2020

Excellent course. The videos were a pleasure to watch, the assignments were clear and allowed you to go as shallow or as in depth as you desired, and the mentors were very helpful.

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201 - 225 of 279 Reviews for Build Basic Generative Adversarial Networks (GANs)

By David M

Jun 9, 2021

e​xcellent course!

By Luis M C S

Nov 9, 2020

hard but worth it

By Josue D G P

Mar 29, 2021

Excelente curso.

By Robin S

Dec 20, 2020

Truly brilliant

By Md A R

Feb 23, 2021

Amazing Course

By 胡冰

May 15, 2021

Great course.

By Wildson B B L

Jan 2, 2021

Great course!

By Victor d l C

Oct 11, 2020

Excellent!!!!

By S A

Jun 1, 2021

It was good!

By Irving G B P

Apr 17, 2021

Amazing work

By Lâm Đ A

Jan 5, 2021

Good course

By Andrew C

Nov 24, 2020

Loved it.

By Black H

Feb 1, 2021

great !!

By Maciej A

Oct 11, 2020

Awesome!

By Ms. N A A

Dec 24, 2020

Thanks

By jiangli

Jan 11, 2021

nice

By Martin J

Nov 23, 2020

An excellent course to bring one up to speed with current developments in GANs. Quite a bit of reading around the subject, in addition to the references provided, is necessary, particularly if you are new to using pytorch or python. But the accompanying Slack support is a life line.

I think this course is even more effective if you have the basics and want to review your state of knowledge and get a bit deeper in to the subject. Otherwise (particularly if you are fitting this in to your other activities), regard the time estimates for the assignments as wildly optimistic: multiply by 150% and use the next highter time unit.

But don't let that put you off, GANs aren't easy whichever way you look at them (unless you invented them)

By Daniel Y

Feb 22, 2021

This is generally a good course to take. However, compare to the Deep Learning Specialization, there are few lacking points. First, the course touches only high-level concepts, which is good in some point but I expected more low-level as well. Second, Sharon speaks way too fast. Later in the course, I set the speed as 0.75x and it was better. I feel like Andrew spoke little slow in Deep Learning courses and now I feel slower is better than fast. Lastly, I hope that the course offers ppt slides available so that we can refer to it later. Moreover, some slow handwriting interaction would be good (like Andrew).

By AhmedAbdel-Aal

Oct 15, 2020

The course is a great introduction to GANs. The explanation was simple and to point and the slides are great with the key points in the first few seconds and also with the summary at the end. However, there are some points that I did not like throughout the course. 1- some concepts that need to be well disgusted are just thrown in a 2 min video, and no matter how I repeat that video, I still can't get it because it is not so intuitive, so some points need more explanation ex: Wasserstein loss. 2- The assignments were not so helpful, I guess you should let the learner to code more than that.

By Vinayak N

Oct 21, 2020

The course is pretty awesome for a beginner who is trying to understand the world of GANs. It provides a good deal of theory lectures and inspires the need for GANs by showing the areas in which they're used with examples.

The exercises, although good aren't sufficient; in the sense we're only required to tweak a very small amount of code and the boilerplate for most code is given. But the exercises as a whole are really cool!

By Sami D

Oct 12, 2020

Great lectures and exercises in "digestible portions". The course explained the GAN basics first and then built upon that base knowledge in a gentle and well though way. You always think that by just reading papers and reviewing reference implementations you can master some new ML-area, but this kind of course is so much more fun with materials, community and support.

By Jeremy S

Mar 19, 2021

This course is great view into GANs. The lectures often briefly review the basics of topics like neural nets or convolutions, yet still offer advanced (optional) lessons and journal articles to read.

I rated 4 stars instead of 5 because I could not find printable/PDF notes for the course, unlike some other courses.

By Yijie X

Nov 24, 2020

Sharon does a great job of teaching concepts, and the course follows well from the Deep Learning Specialization. You will find that while the code exercises start out facile, you will require some help on the Slack channel, almost no code support is given in course (in contrast to Nanodegree programs).

By Sandeep W

Oct 4, 2020

I think this is a bit too basic, there are some areas where i believe some more maths and theory might be appropriate. IE specifically the video section prior to W4B programming exercise with the latent z space manipulation to target disentanglement of features.

By GAURAV A

Oct 7, 2020

Good for basic GAN knowledge. Good for Pytorch knowhow, if you are new to it. Concepts are explained in easy to understand way.

More mathematical explanations on probability distributions of real and fake images, Their distances would have been better