About this Course

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Intermediate Level
  • Basic calculus, linear algebra, stats
  • Grasp of AI, deep learning & CNNs
  • Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
Approx. 28 hours to complete
English

Skills you will gain

Controllable GenerationWGANsConditional GenerationComponents of GANsDCGANs
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
  • Basic calculus, linear algebra, stats
  • Grasp of AI, deep learning & CNNs
  • Intermediate Python & experience with DL frameworks (TF / Keras / PyTorch)
Approx. 28 hours to complete
English

Offered by

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DeepLearning.AI

Syllabus - What you will learn from this course

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Week
1

Week 1

7 hours to complete

Week 1: Intro to GANs

7 hours to complete
10 videos (Total 58 min), 7 readings, 1 quiz
10 videos
Welcome to Week 154s
Generative Models8m
Real Life GANs5m
Intuition Behind GANs5m
Discriminator5m
Generator7m
BCE Cost Function6m
Putting It All Together5m
(Optional) Intro to PyTorch6m
7 readings
Syllabus5m
Connect with your mentors and fellow learners on Slack!5m
Check out some non-existent people!5m
Pre-trained Model Exploration30m
Inputs to a Pre-trained GAN 30m
Works Cited10m
How to Refresh your Workspace10m
Week
2

Week 2

5 hours to complete

Week 2: Deep Convolutional GANs

5 hours to complete
9 videos (Total 37 min), 3 readings, 1 quiz
9 videos
Activations (Basic Properties)4m
Common Activation Functions6m
Batch Normalization (Explained)5m
Batch Normalization (Procedure)5m
Review of Convolutions3m
Padding and Stride3m
Pooling and Upsampling5m
Transposed Convolutions2m
3 readings
(Optional) A Closer Look at Transposed Convolutions40m
(Optional) The DCGAN Paper40m
Works Cited5m
Week
3

Week 3

8 hours to complete

Week 3: Wasserstein GANs with Gradient Penalty

8 hours to complete
7 videos (Total 26 min), 3 readings, 1 quiz
7 videos
Mode Collapse4m
Problem with BCE Loss3m
Earth Mover’s Distance2m
Wasserstein Loss4m
Condition on Wasserstein Critic3m
1-Lipschitz Continuity Enforcement5m
3 readings
(Optional) The WGAN and WGAN-GP Papers2h
(Optional) WGAN Walkthrough1h
Works Cited5m
Week
4

Week 4

9 hours to complete

Week 4: Conditional GAN & Controllable Generation

9 hours to complete
9 videos (Total 27 min), 4 readings, 2 quizzes
9 videos
Conditional Generation: Intuition2m
Conditional Generation: Inputs4m
Controllable Generation3m
Vector Algebra in the Z-Space3m
Challenges with Controllable Generation2m
Classifier Gradients2m
Disentanglement4m
Conclusion of Course 11m
4 readings
(Optional) The Conditional GAN Paper30m
(Optional) An Example of a Controllable GAN1h 30m
Works Cited5m
Acknowledgments5m

Reviews

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About the Generative Adversarial Networks (GANs) Specialization

Generative Adversarial Networks (GANs)

Frequently Asked Questions

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