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[ DevCourseWeb com ] Udemy - Introduction to Generative Adversarial Networks with PyTorch
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1. Course Agenda
1. Course Agenda.mp4
1. Course Agenda.srt
2. Introduction to PyTorch for GANs
1. Notebook Versioning Notice.html
2. PyTorch Forward and Backward Propagation.mp4
2. PyTorch Forward and Backward Propagation.srt
2.1 Course Discussions Channel on Slack.html
2.3 Slack Channel Inactive.html
3. PyTorch Forward and Backward Propagation.html
4. PyTorch Autograd Mechanism.mp4
4. PyTorch Autograd Mechanism.srt
4.1 Revised source code for section 1.html
5. PyTorch Autograd Mechanism.html
6. PyTorch Custom Loss Function.mp4
6. PyTorch Custom Loss Function.srt
6.1 Code Errata.html
6.2 Reading Assignment On Loss Functions for Deep Neural Networks in Classification Katarzyna Janocha, Wojciech Marian Czarnecki.html
7. PyTorch Custom Loss Function.html
Section 1.ipynb
3. Generate Handwritten Digits with Vanilla GAN
1. Introduction to GANs.mp4
1. Introduction to GANs.srt
10. [Coding Exercise] GAN Evaluation Metrics FID Score.mp4
10. [Coding Exercise] GAN Evaluation Metrics FID Score.srt
11. GAN Evaluation Metrics.html
2. Introduction to GANs.html
3. Working of GAN Loss Function.mp4
3. Working of GAN Loss Function.srt
4. Working of GAN Loss Function.html
5. Implementing GAN Training Methodology.mp4
5. Implementing GAN Training Methodology.srt
6. Implementing GAN Training Methodology.html
7. Implement Vanilla GAN on MNIST Dataset to Generate Digits.mp4
7. Implement Vanilla GAN on MNIST Dataset to Generate Digits.srt
8. Implement Vanilla GAN on MNIST Dataset to Generate Digits.html
9. [Coding Exercise] GAN Evaluation Metrics Inception Score.mp4
9. [Coding Exercise] GAN Evaluation Metrics Inception Score.srt
Section 2 - Exercise.ipynb
Section 2.ipynb
[Coding_Exercise]_GAN_Evaluation_Metrics_FID_Score.ipynb
[Coding_Exercise]_GAN_Evaluation_Metrics_Inception_Score.ipynb
4. Generate Specific Digits with Conditional GAN
1. Introduction to Conditional GANs.mp4
1. Introduction to Conditional GANs.srt
10. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.html
2. Introduction to Conditional GANs.html
3. Implement Conditional GAN on MNIST Dataset.mp4
3. Implement Conditional GAN on MNIST Dataset.srt
4. Implement Conditional GAN on MNIST Dataset.html
5. Working of Wasserstein Loss Function.mp4
5. Working of Wasserstein Loss Function.srt
6. Working of Wasserstein Loss Function.html
7. Implement Wasserstein Loss Function.mp4
7. Implement Wasserstein Loss Function.srt
8. Implement Wasserstein Loss Function.html
9. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.mp4
9. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.srt
Section 3 - Bonus - Learning Rate Decay.ipynb
Section 3 - Lecture 2 - FashionMNIST Excercise.ipynb
Section 3 - Lecture 2.ipynb
Section 3 - Lecture 4.ipynb
Section_3_Lecture_5_GP_WGAN.ipynb
5. Diving Deeper with a Deep Convolutional GAN
1. Introduction to DC-GANs.mp4
1. Introduction to DC-GANs.srt
2. Introduction to DC-GANs.html
3. Implement DC-GAN on UC Birds Dataset.mp4
3. Implement DC-GAN on UC Birds Dataset.srt
4. Implement DC-GAN on UC Birds Dataset.html
5. Working of Multi-way Loss Function.mp4
5. Working of Multi-way Loss Function.srt
6. Working of Multi-way Loss Function.html
7. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.mp4
7. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.srt
8. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.html
Section 4 - Lecture 2.ipynb
Section 4 - Lecture 4.ipynb
6. Generate Realistic Human Faces with Progressive GAN
1. Introduction to Progressive GANs.mp4
1. Introduction to Progressive GANs.srt
2. Introduction to Progressive GANs.html
3. Implement Progressive GANs on Celebs Dataset.mp4
3. Implement Progressive GANs on Celebs Dataset.srt
4. Implement Progressive GANs on Celebs Dataset.html
5. Hints, Tips, and Tricks for GAN Training.mp4
5. Hints, Tips, and Tricks for GAN Training.srt
6. Hints, Tips, and Tricks for GAN Training.html
Section 5 - Lecture 2.ipynb
7. Generate Videos from Other Videos
1. Introduction to U-NET Architecture.mp4
1. Introduction to U-NET Architecture.srt
10. Working of Vid2Vid GAN.html
11. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.mp4
11. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.srt
12. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.html
13. Conclusion, Next Steps, and Future Directions.mp4
13. Conclusion, Next Steps, and Future Directions.srt
14. Conclusion, Next Steps, and Future Directions.html
2. Introduction to U-NET Architecture.html
3. Working of Pix2Pix GAN and CycleGAN.mp4
3. Working of Pix2Pix GAN and CycleGAN.srt
4. Working of Pix2Pix GAN and CycleGAN.html
5. [Coding Exercise] Hands-on Pix2Pix GAN.mp4
5. [Coding Exercise] Hands-on Pix2Pix GAN.srt
6. [Coding Exercise] Hands-on Pix2Pix GAN.html
7. [Coding Exercise] Hands-on CycleGAN.mp4
7. [Coding Exercise] Hands-on CycleGAN.srt
8. [Coding Exercise] Hands-on CycleGAN.html
9. Working of Vid2Vid GAN.mp4
9. Working of Vid2Vid GAN.srt
Section_6_CycleGAN.ipynb
Section_6_Pix2Pix_GAN.ipynb
8. Appendix Interesting Readings
1. LeakGAN Long Text Generation via Adversarial Training with Leaked Information.html
1.1 Arxiv Paper.html
2. MaskGAN Towards Diverse and Interactive Facial Image Manipulation.html
2.1 Arxiv Paper.html
3. MGAN Markovian Generative Adversarial Networks.html
3.1 Arxiv Paper.html
4. GraphGAN Graph Representation Learning with Generative Adversarial Nets.html
4.1 Arxiv Paper.html
Bonus Resources.txt
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