Build Basic Generative Adversarial Networks (GANs)
Master fundamental GAN concepts and architectures, from basic implementations to conditional generation using PyTorch.
Master fundamental GAN concepts and architectures, from basic implementations to conditional generation using PyTorch.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Generative Adversarial Networks (GANs) Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Build and train basic GANs using PyTorch
Implement advanced architectures including DCGANs and WGANs
Master conditional generation techniques
Understand GAN components and training dynamics
Optimize GAN performance and stability
Develop skills in controllable image generation
Skills you'll gain
This course includes:
2.4 Hours PreRecorded video
4 programming assignments
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FullTime access
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There are 4 modules in this course
This comprehensive course introduces students to Generative Adversarial Networks (GANs), covering both theoretical foundations and practical implementations. The curriculum progresses from basic GAN architectures to advanced concepts like DCGANs, WGANs, and conditional generation. Students learn through hands-on PyTorch implementations, exploring real-world applications while gaining insights into GAN components, training dynamics, and potential challenges. The course emphasizes practical skills in building and optimizing GANs for image generation tasks.
Week 1: Intro to GANs
Module 1 · 5 Hours to complete
Week 2: Deep Convolutional GANs
Module 2 · 6 Hours to complete
Week 3: Wasserstein GANs with Gradient Penalty
Module 3 · 8 Hours to complete
Week 4: Conditional GAN & Controllable Generation
Module 4 · 9 Hours to complete
Fee Structure
Instructors
Eric Zelikman: Innovating Deep Learning and Representation Learning
Eric Zelikman is a deep learning engineer with a keen interest in how algorithms can learn meaningful representations. A recent graduate of Stanford University's Symbolic Systems program, he focuses on developing efficient, robust, and disentangled representations within the field of machine learning. Zelikman believes that insights from machine learning can inform broader human challenges, aiming to leverage this technology to address significant global issues. He is currently engaged with DeepLearning.AI and has contributed to various research initiatives that explore the intersections of language models and reasoning.
AI Pioneer Bridges Classical and Modern Learning Through Generative AI Innovation
Sharon Zhou is a distinguished AI researcher and entrepreneur who uniquely combines classical education with cutting-edge technology. As co-founder and CEO of Lamini, she develops innovative LLM solutions while maintaining her role as an influential educator through DeepLearning.AI. A Stanford Ph.D. graduate advised by Andrew Ng, she created one of Coursera's largest classes on Generative Adversarial Networks, reaching over 250,000 students. Zhou holds the distinction of being the first to major in both Classics and Computer Science at Harvard University, graduating summa cum laude in both fields. Named in MIT Technology Review's 35 Under 35, she serves as an AI advisor to Washington D.C. policymakers while advancing her mission of making AI technology accessible to everyone. Her research spans theoretical and applied AI in medicine, climate, and social good, building on her experience as a former machine learning product manager at Google
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