Sharon Zhou

Sharon Zhou is a CS PhD candidate at Stanford University, advised by Andrew Ng. Sharon's work in AI spans from the theoretical to the applied — in medicine, climate, and more broadly, social good. Previously a machine learning product manager at Google and a few startups, Sharon is a Harvard graduate in CS and Classics. She likes humans more than AI, though GANs occupy a special place in her heart.

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Build Basic Generative Adversarial Networks (GANs) (Coursera) Coursera
DeepLearning.AI

Build Basic Generative Adversarial Networks (GANs) (Coursera)

Discover the power of Generative Adversarial Networks (GANs) in this introductory course. Whether you're new to machine learning or looking to deepen your expertise, this course will guide you through understanding GAN basics, their real-world applications, and implementing various architectures to generate compelling data samples. Start your journey into the fascinating world of GANs today!

Jun 8th 2026
4 Weeks
Build Better Generative Adversarial Networks (GANs) (Coursera) Coursera
DeepLearning.AI

Build Better Generative Adversarial Networks (GANs) (Coursera)

Dive into the world of Generative Adversarial Networks (GANs) with our expert-led course designed to help you build better, more sophisticated GAN models. From understanding the challenges in evaluating GAN performance to implementing state-of-the-art techniques like StyleGAN, this course equips you with the skills needed to create high-fidelity and diverse generative models.

Jun 8th 2026
3 Weeks
Apply Generative Adversarial Networks (GANs) (Coursera) Coursera
DeepLearning.AI

Apply Generative Adversarial Networks (GANs) (Coursera)

Dive into the world of Generative Adversarial Networks (GANs) with this in-depth online course. Whether you're interested in enhancing data privacy, augmenting datasets, or exploring creative applications like image translation, this course provides a solid foundation and practical experience with GANs. Learn to implement Pix2Pix and CycleGAN models, and understand the nuances between paired and unpaired image-to-image translations.

Jun 8th 2026
3 Weeks
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