DeepLearning.AI - Build Better Generative Adversarial Networks (GANs)
- Offered byCoursera
Build Better Generative Adversarial Networks (GANs) at Coursera Overview
Duration | 28 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Build Better Generative Adversarial Networks (GANs) at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Build Better Generative Adversarial Networks (GANs) at Coursera Course details
- In this course, you will:
- - Assess the challenges of evaluating GANs and compare different generative models
- - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs
- - Identify sources of bias and the ways to detect it in GANs
- - Learn and implement the techniques associated with the state-of-the-art StyleGANs
- 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-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
- Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.
- This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.
Build Better Generative Adversarial Networks (GANs) at Coursera Curriculum
Week 1: Evaluation of GANs
Welcome to Course 2
Welcome to Week 1
Evaluation
Comparing Images
Feature Extraction
Inception-v3 and Embeddings
Fréchet Inception Distance (FID)
Inception Score
Sampling and Truncation
Precision and Recall
Syllabus
Connect with your mentors and fellow learners on Slack!
(Optional) A Closer Look at Inception Score
(Optional) HYPE!!
(Optional) More on Precision and Recall
(Optional) Recap of FID and IS
Works Cited
Week 2: GAN Disadvantages and Bias
Welcome to Week 2
Disadvantages of GANs
Alternatives to GANs
Intro to Machine Bias
Defining Fairness
Ways Bias is Introduced
(Optional Notebook) Score-based Generative Modeling
Machine Bias
Fairness Definitions
A Survey on Bias and Fairness in Machine Learning
Finding Bias
Works Cited
Analyzing Bias
Week 3: StyleGAN and Advancements
Welcome to Week 3
GAN Improvements
StyleGAN Overview
Progressive Growing
Noise Mapping Network
Adaptive Instance Normalization (AdaIN)
Style and Stochastic Variation
Putting It All Together
Conclusion of Course 2
(Optional) The StyleGAN Paper
(Optional) StyleGAN Walkthrough and Beyond
(Optional Notebook) Finetuning Notebook: FreezeD
Works Cited
Acknowledgments
Build Better Generative Adversarial Networks (GANs) at Coursera Admission Process
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