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CourseraBuild Basic Generative Adversarial Networks (GANs)
4.7(2,004)79k studentsINTERMEDIATEEnglish
Korshub's take
Building the models behind AI image generation
Part of the DeepLearning.AI GANs specialization, this course gets hands-on with the architecture that made much of modern AI image generation possible. It's implementation-focused — you're building GANs in PyTorch, not just reading about how they work.
What you'll build
- Multiple GAN architectures, from foundational to more advanced designs
- Conditional GANs that generate examples from specific categories, not just random output
- Convolutional neural network components underneath generative models
It's set at the intermediate level, so some deep learning and PyTorch familiarity will help before diving in. Nearly 80,000 learners have taken it, and the inclusion of responsible AI and data ethics alongside the technical architecture is a useful counterweight — generative image models raise real questions this course doesn't skip.
About this course
In this course, you will:
- Learn about GANs and their applications
- Understand the intuition behind the fundamental components of GANs
- Explore and implement multiple GAN architectures
- Build conditional GANs capable of generating examples from determined categories
The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-unde
Skills you'll gain: Generative Adversarial Networks (GANs), Generative AI, Generative Model Architectures, PyTorch (Machine Learning Library), Deep Learning, Convolutional Neural Networks, Responsible AI, Data Ethics, Model Training, Model Evaluation.
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