{ "id": 180515, "title": "All you wanted to know about GANs and you were too afraid to ask", "url": "/competitions/gan-getting-started/discussion/180515", "author_name": "Gabriel Preda", "post_date": "2020-09-05T10:38:26.769000", "votes": 49, "comment_count": 11, "views": 0, "content": "

\"\"

\n

Scientific Papers

\n\n

Courses

\n\n

Books, Blogs, Popularization Articles, Tutorials

\n", "messages": [ { "id": 3093934, "postDate": "2025-01-11T13:27:19.837Z", "content": "

I made a little \"modern\" GAN introduction notebook too. It's for generating images of dogs, good resource if you wan't to get started with GANs using PyTorch.

\n

I forked it from a very popular notebook which wasn't working well, since it is so old, so I updated it and fixed up some issues: kipshidze/modern-gan-introduction Maybe someone finds it useful :D

", "rawMarkdown": "I made a little \"modern\" GAN introduction notebook too. It's for generating images of dogs, good resource if you wan't to get started with GANs using **PyTorch**.\n\nI forked it from a very popular notebook which wasn't working well, since it is so old, so I updated it and fixed up some issues: [kipshidze/modern-gan-introduction](https://www.kaggle.com/code/kipshidze/modern-gan-introduction) Maybe someone finds it useful :D", "votes": 1 }, { "id": 999080, "postDate": "2020-09-05T10:38:26.770Z", "content": "

\"\"

\n

Scientific Papers

\n\n

Courses

\n\n

Books, Blogs, Popularization Articles, Tutorials

\n", "rawMarkdown": "![](https://miro.medium.com/max/3930/1*7m2acv_MegOgmmUdYbN_GQ.jpeg)\n\n### Scientific Papers\n\n* [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) \n* [Generative Adversarial Networks: An Overview](https://arxiv.org/pdf/1710.07035.pdf)\n* [Are GANs Created Equal? A Large-Scale Study](https://arxiv.org/abs/1711.10337) \n* [A Large-Scale Study on Regularization and Normalization in GANs](https://arxiv.org/abs/1807.04720) \n* [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://arxiv.org/abs/2006.05132) \n* [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://arxiv.org/abs/2001.06937) \n* [Generative Adversarial Network in Medical Imaging: A Review](https://arxiv.org/abs/1809.07294) \n* [Regularization And Normalization For Generative Adversarial Networks: A Review](https://arxiv.org/abs/2008.08930) \n\n\n### Courses \n\n* [Deep Learning: GANs and Variational Autoencoders](https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/) \n* [Generative Adversarial Networks](https://developers.google.com/machine-learning/gan) \n* [Andrew Ng, CS229 Lecture notes, Generative Learning algorithms](http://cs229.stanford.edu/notes/cs229-notes2.pdf) \n* [Generate Synthetic Images with DCGANs in Keras](https://www.coursera.org/projects/generative-adversarial-networks-keras)\n\n### Books, Blogs, Popularization Articles, Tutorials \n\n* [10 Free Resources To Learn GAN In 2020](https://analyticsindiamag.com/10-free-resources-to-learn-gan-in-2020/) \n* [Generative Models](https://openai.com/blog/generative-models/) \n* [GANs in Action, Deep learning with Generative Adversarial Networks](https://www.manning.com/books/gans-in-action#toc) \n* [GAN — What is Generative Adversarial Networks GAN?](https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09) \n* [Generative Adversarial Network (GAN) for Dummies — A Step By Step Tutorial](https://towardsdatascience.com/generative-adversarial-network-gan-for-dummies-a-step-by-step-tutorial-fdefff170391) \n* [Deep Diving into GANs: from theory to production](https://github.com/zurutech/gans-from-theory-to-production#deep-diving-into-gans-from-theory-to-production) \n* [Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)](https://www.youtube.com/watch?v=HGYYEUSm-0Q) \n* [David Foster, Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play](https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947/ref=sr_1_2?dchild=1&keywords=GANs-Action-learning-Generative-Adversarial&qid=1599732764&sr=8-2) \n* [Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras](https://www.amazon.com/Generative-Adversarial-Networks-Cookbook-generative/dp/1789139902/ref=sr_1_3?dchild=1&keywords=GANs-Action-learning-Generative-Adversarial&qid=1599732764&sr=8-3) \n* [Learning Generative Adversarial Networks: Next-generation deep learning simplified](https://www.amazon.com/Learning-Generative-Adversarial-Networks-Next-generation/dp/1788396413/ref=as_li_ss_tl?keywords=Learning+Generative+Adversarial+Networks&qid=1559092065&s=books&sr=1-1&linkCode=sl1&tag=inspiredalgor-20&linkId=6eb66b672e268abb52b6c5a302c7d4f5&language=en_US)\n\n\n", "votes": 48 }, { "id": 1021366, "postDate": "2020-09-21T19:36:22.783Z", "content": "

Nice, helpful!

", "rawMarkdown": "Nice, helpful!", "votes": 1 }, { "id": 1006165, "postDate": "2020-09-11T04:57:28.683Z", "content": "

This is an amazing compilation! Thank you so much for putting it all together!

", "rawMarkdown": "This is an amazing compilation! Thank you so much for putting it all together!", "votes": 1 }, { "id": 1001086, "postDate": "2020-09-07T04:11:52.760Z", "content": "

Thank you @gpreda for these well-informed reading resources😃!

", "rawMarkdown": "Thank you @gpreda for these well-informed reading resources😃!", "votes": 1 }, { "id": 1008705, "postDate": "2020-09-13T10:27:50.230Z", "content": "

Thanks a lot for these resources! Great way of getting started.

", "rawMarkdown": "Thanks a lot for these resources! Great way of getting started.", "votes": 2 }, { "id": 2658258, "postDate": "2024-02-19T05:20:31.083Z", "content": "

Helpful blog!

", "rawMarkdown": "Helpful blog!" }, { "id": 2300716, "postDate": "2023-06-13T11:36:23.660Z", "content": "

helpful blog

", "rawMarkdown": "helpful blog" }, { "id": 999084, "postDate": "2020-09-05T10:43:53.740Z", "rawMarkdown": "", "isDeleted": true }, { "id": 2467832, "postDate": "2023-10-04T22:12:29.423Z", "content": "

Thanks, this was really helpful.

", "rawMarkdown": "Thanks, this was really helpful." }, { "id": 2290905, "postDate": "2023-06-07T07:35:48.877Z", "content": "

Thanks, helpful1

", "rawMarkdown": "Thanks, helpful1" }, { "id": 2192016, "postDate": "2023-03-22T11:24:46.317Z", "content": "

Thank you @gpreda, helpfull!

", "rawMarkdown": "Thank you @gpreda, helpfull! " } ], "comments": [ { "id": 3093934, "author_name": "Nick Kipshidze", "author_url": "", "post_date": "2025-01-11T13:27:19.837000", "content": "

I made a little \"modern\" GAN introduction notebook too. It's for generating images of dogs, good resource if you wan't to get started with GANs using PyTorch.

\n

I forked it from a very popular notebook which wasn't working well, since it is so old, so I updated it and fixed up some issues: kipshidze/modern-gan-introduction Maybe someone finds it useful :D

", "votes": 1, "replies": [] }, { "id": 1021366, "author_name": "Paul Hetherington [Neuro Ai]", "author_url": "", "post_date": "2020-09-21T19:36:22.783000", "content": "

Nice, helpful!

", "votes": 1, "replies": [] }, { "id": 1006165, "author_name": "Aditya Baurai", "author_url": "", "post_date": "2020-09-11T04:57:28.683000", "content": "

This is an amazing compilation! Thank you so much for putting it all together!

", "votes": 1, "replies": [] }, { "id": 1001086, "author_name": "Beans", "author_url": "", "post_date": "2020-09-07T04:11:52.760000", "content": "

Thank you @gpreda for these well-informed reading resources😃!

", "votes": 1, "replies": [] }, { "id": 1008705, "author_name": "Oscar Rovira [Neuro Ai]", "author_url": "", "post_date": "2020-09-13T10:27:50.230000", "content": "

Thanks a lot for these resources! Great way of getting started.

", "votes": 2, "replies": [] }, { "id": 2658258, "author_name": "Riya Raizada", "author_url": "", "post_date": "2024-02-19T05:20:31.083000", "content": "

Helpful blog!

", "votes": 0, "replies": [] }, { "id": 2300716, "author_name": "Waquar Haseeb", "author_url": "", "post_date": "2023-06-13T11:36:23.660000", "content": "

helpful blog

", "votes": 0, "replies": [] }, { "id": 999084, "author_name": "", "author_url": "", "post_date": "2020-09-05T10:43:53.740000", "content": "", "votes": 0, "replies": [] }, { "id": 2467832, "author_name": "Mùneeb Ahméd", "author_url": "", "post_date": "2023-10-04T22:12:29.423000", "content": "

Thanks, this was really helpful.

", "votes": 0, "replies": [] }, { "id": 2290905, "author_name": "MINH DANG", "author_url": "", "post_date": "2023-06-07T07:35:48.877000", "content": "

Thanks, helpful1

", "votes": 0, "replies": [] }, { "id": 2192016, "author_name": "yeongseop lee", "author_url": "", "post_date": "2023-03-22T11:24:46.317000", "content": "

Thank you @gpreda, helpfull!

", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "3093934": "I made a little \"modern\" GAN introduction notebook too. It's for generating images of dogs, good resource if you wan't to get started with GANs using **PyTorch**.\n\nI forked it from a very popular notebook which wasn't working well, since it is so old, so I updated it and fixed up some issues: [kipshidze/modern-gan-introduction](https://www.kaggle.com/code/kipshidze/modern-gan-introduction) Maybe someone finds it useful :D", "999080": "![](https://miro.medium.com/max/3930/1*7m2acv_MegOgmmUdYbN_GQ.jpeg)\n\n### Scientific Papers\n\n* [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) \n* [Generative Adversarial Networks: An Overview](https://arxiv.org/pdf/1710.07035.pdf)\n* [Are GANs Created Equal? A Large-Scale Study](https://arxiv.org/abs/1711.10337) \n* [A Large-Scale Study on Regularization and Normalization in GANs](https://arxiv.org/abs/1807.04720) \n* [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://arxiv.org/abs/2006.05132) \n* [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://arxiv.org/abs/2001.06937) \n* [Generative Adversarial Network in Medical Imaging: A Review](https://arxiv.org/abs/1809.07294) \n* [Regularization And Normalization For Generative Adversarial Networks: A Review](https://arxiv.org/abs/2008.08930) \n\n\n### Courses \n\n* [Deep Learning: GANs and Variational Autoencoders](https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/) \n* [Generative Adversarial Networks](https://developers.google.com/machine-learning/gan) \n* [Andrew Ng, CS229 Lecture notes, Generative Learning algorithms](http://cs229.stanford.edu/notes/cs229-notes2.pdf) \n* [Generate Synthetic Images with DCGANs in Keras](https://www.coursera.org/projects/generative-adversarial-networks-keras)\n\n### Books, Blogs, Popularization Articles, Tutorials \n\n* [10 Free Resources To Learn GAN In 2020](https://analyticsindiamag.com/10-free-resources-to-learn-gan-in-2020/) \n* [Generative Models](https://openai.com/blog/generative-models/) \n* [GANs in Action, Deep learning with Generative Adversarial Networks](https://www.manning.com/books/gans-in-action#toc) \n* [GAN — What is Generative Adversarial Networks GAN?](https://medium.com/@jonathan_hui/gan-whats-generative-adversarial-networks-and-its-application-f39ed278ef09) \n* [Generative Adversarial Network (GAN) for Dummies — A Step By Step Tutorial](https://towardsdatascience.com/generative-adversarial-network-gan-for-dummies-a-step-by-step-tutorial-fdefff170391) \n* [Deep Diving into GANs: from theory to production](https://github.com/zurutech/gans-from-theory-to-production#deep-diving-into-gans-from-theory-to-production) \n* [Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)](https://www.youtube.com/watch?v=HGYYEUSm-0Q) \n* [David Foster, Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play](https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947/ref=sr_1_2?dchild=1&keywords=GANs-Action-learning-Generative-Adversarial&qid=1599732764&sr=8-2) \n* [Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras](https://www.amazon.com/Generative-Adversarial-Networks-Cookbook-generative/dp/1789139902/ref=sr_1_3?dchild=1&keywords=GANs-Action-learning-Generative-Adversarial&qid=1599732764&sr=8-3) \n* [Learning Generative Adversarial Networks: Next-generation deep learning simplified](https://www.amazon.com/Learning-Generative-Adversarial-Networks-Next-generation/dp/1788396413/ref=as_li_ss_tl?keywords=Learning+Generative+Adversarial+Networks&qid=1559092065&s=books&sr=1-1&linkCode=sl1&tag=inspiredalgor-20&linkId=6eb66b672e268abb52b6c5a302c7d4f5&language=en_US)\n\n\n", "1021366": "Nice, helpful!", "1006165": "This is an amazing compilation! Thank you so much for putting it all together!", "1001086": "Thank you @gpreda for these well-informed reading resources😃!", "1008705": "Thanks a lot for these resources! Great way of getting started.", "2658258": "Helpful blog!", "2300716": "helpful blog", "999084": "", "2467832": "Thanks, this was really helpful.", "2290905": "Thanks, helpful1", "2192016": "Thank you @gpreda, helpfull! " } }