Build Better Generative Adversarial Networks (GANs)
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- المدة 29 ساعات hours
- الطبع بواسطة DeepLearning.AI
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Offered by
عن
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.الوحدات
Evaluation of GANs
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10
Videos
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8
Readings
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1
Lab
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1
Programming
- Fréchet Inception Distance
1
Labs
- (Optional) Perceptual Path Length
10
Videos
- 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
8
Readings
- Syllabus
- [IMPORTANT] Have questions, issues or ideas? Join our Forum!
- (Optional) A Closer Look at Inception Score
- (Optional) HYPE!!
- (Optional) More on Precision and Recall
- (Optional) Lecture Notes W1
- (Optional) Recap of FID and IS
- Works Cited
GAN Disadvantages and Bias
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6
Videos
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9
Readings
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1
Lab
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1
Programming
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1
Assignment
- Bias
1
Assignment
- Analyzing Bias
1
Labs
- Alternatives: Variational Autoencoders (VAEs)
6
Videos
- Welcome to Week 2
- Disadvantages of GANs
- Alternatives to GANs
- Intro to Machine Bias
- Defining Fairness
- Ways Bias is Introduced
9
Readings
- (Optional Notebook) Score-based Generative Modeling
- Machine Bias
- Fairness Definitions
- A Survey on Bias and Fairness in Machine Learning
- Finding Bias
- (Optional) Lecture Notes W2
- (Optional Notebook) GAN Debiasing
- Works Cited
- (Optional Notebook) NeRF: Neural Radiance Fields
StyleGAN and Advancements
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9
Videos
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6
Readings
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2
Lab
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1
Programming
- Components of StyleGAN
2
Labs
- (Optional) Components of StyleGAN2
- (Optional) Components of BigGAN
9
Videos
- 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
6
Readings
- (Optional) Lecture Notes W3
- [IMPORTANT] Reminder about end of access to Lab Notebooks
- (Optional) The StyleGAN Paper
- (Optional) StyleGAN Walkthrough and Beyond
- Works Cited
- Acknowledgments
Auto Summary
Unlock the potential of Generative Adversarial Networks (GANs) with this comprehensive course in Data Science & AI. Led by DeepLearning.AI on Coursera, you'll explore GAN challenges, evaluate models using the Fréchet Inception Distance (FID) method, and tackle bias detection. Dive into state-of-the-art StyleGANs, learn to train models with PyTorch, and generate images. This 1740-minute Professional level course is perfect for learners of all levels, offering hands-on experience and insights into GANs' social implications. Available with a Starter subscription, it's ideal for those looking to advance their AI skills.
Sharon Zhou
Eda Zhou
Eric Zelikman