Review: This course was a complete waste of time. Weeks 1–3 promised to teach “state‑of‑the‑art GANs” but delivered little more than hype, repetition, and links to external papers. Instead of actual instruction, the content was padded with congratulatory mimicry passages, optional labs, and “works cited” lists that simply redirected learners to arXiv papers, Medium blogs, or GitHub repos. Assignments demanded complex PyTorch implementations (StyleGAN, BigGAN, etc.), yet the lectures and notes provided no scaffolding or walkthroughs. The most advanced material was labeled “optional” and outsourced to dense research papers or gated blog posts, leaving learners unsupported. Even the acknowledgments list dozens of contributors, but the output is hollow — exposure without teaching. The result is credential theater: you’re told you’ve mastered GANs, but the course never actually teaches them. If you want to truly understand GAN architectures, you’ll need to study the original papers yourself. This specialization performs instruction rather than delivering it, and Weeks 1–3 prove that certification is prioritized over comprehension. Bottom line: Don’t waste your time. One star.