Mustafa Qamaruddin
8.3/10
(Our Score)
Product is rated as #192 in category Machine Learning
Master the basic building blocks of modern generative adversarial networks with a unique course that reviews the most recent research papers in GANs and at the same time gives the learner a very detailed hands–on experience in the topic. Start by learning the very basics of how GANs work and incrementally learn more cleverly crafted techniques that enhance your models from the basic GANs towards the more advanced Progressive Growing of GANs. On the journey, you shall learn a fair amount of deep learning concepts with an adequate discussion of the mathematics behind the modern models.
Instructor Details
Votes: 0
Courses : 1
Specification: Introduction to Generative Adversarial Networks with PyTorch
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8 reviews for Introduction to Generative Adversarial Networks with PyTorch
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| Price | $14.99 |
|---|---|
| Provider | |
| Duration | 6 hours |
| Year | 2021 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
Introduction to Generative Adversarial Networks with PyTorch
$14.99
Mubashar Khawar –
,m
Eman Mohammed –
Excellent course
Mohamed Ibrahim –
Really Great course
Aniket Shinde –
Yes,it is providing me with all inputs.Also want to talk with the author.How can I connect to him?
Nd ye Maguette MBAYE –
Mustafa is a great lecturer, he explains very smoothly and clearly all the concepts. I wasn’t familar with PyTorch but his explanation helps me understand PyTorch better. Moreover the ressources are great and very intuitive and help keep track with the course.
Amy Badr El Din –
Excellent Course.
Mahmoud Naguib –
It was a very nice course.
Andrii Torchylo –
Thank Mustafa for the really nice course. I liked the fact that you’ve included various visualization technics and explained many utilities for preprocessing! I think that Jupyter notebooks were very useful for understanding the concepts and it definitely helped me to learn more than by simply looking at the papers. However, I would like to see more visuals and text descriptions in notebooks to better understand the code. Also, I think it would be great if you could leave some code blocks empty for students to implement themselves and gave us some additional tests below those blocks to verify that our implementation is working properly. Additionally, I would consider adding some lectures about math in GANs. Coming with no background in GANs and having only a little experience in coding neural nets, it was a little hard for me to understand some math concepts like ones in Wasserstein GANs. Overall, it was a great course, and I thank you for your time answering my questions in comments section! It was a pleasure taking this course