This course presents itself as a structured introduction to GANs, but the instructional delivery is extremely difficult to follow. The primary instructor speaks at a pace closer to an auctioneer than a teacher, rushing through key concepts so quickly that the material becomes inaccessible. Important ideas are delivered with the tone and simplicity of a kindergarten art lesson, while the underlying technical depth is never actually explained. The result is a mismatch between the seriousness of the topic and the superficial way it is presented. The content itself is also significantly outdated. Much of the material reflects techniques and workflows from nearly a decade ago, long before modern diffusion models, transformer‑based architectures, and current generative pipelines became standard. Many of the tasks demonstrated in the course can now be performed more effectively by off‑the‑shelf AI tools, yet the course does not acknowledge this shift or update its framing. This makes the curriculum feel disconnected from the state of the field in 2026. The assignments do not match the content taught in the videos. Several graded tasks require knowledge that never appears in the lectures, forcing learners to rely on external papers, GitHub repositories, and prior experience just to complete the work. The “Works Cited” section unintentionally reveals this: nearly all meaningful explanations and architectures come from outside sources, not the course itself. The acknowledgements list is extremely long, yet the final product does not reflect the work of a large coordinated team. The content is inconsistent, lacks depth, and does not align with the assignments. The gap between the number of contributors and the quality of the curriculum is striking. The mentorship invitation at the end is the clearest indicator of the underlying structure. Instead of improving the course, learners are encouraged to volunteer to guide other students, test other specializations, and provide support — all unpaid. This reframes instructional gaps as a “community opportunity,” and confirms that the course relies on volunteer labor to compensate for missing or unclear teaching. Overall, the specialization feels like a patchwork of external materials held together by marketing language rather than a coherent educational design. It needs significant revision to meet the expectations set by its presentation.

The instructor did not explain things very deeply! I was expecting more technical lectures on how to deep dive into the topic rather than just providing high level overview of the GANs variants. Some optional notebooks definitely worth to be part of the lectures for more motivations, such as pix2pixHD probably as one lecture. The specialization could potentially divided into several more courses with more detailed and technical lectures. The workflow was was not easy to follow as in the level of 2nd and 3rd courses are extremely higher than course 1 which was mainly supposed to provided overview of the GAN and inspire a potential learner to continue further.