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1. Course Agenda.mp4
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MP4
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91.5 MB
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1. Course Agenda.srt
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9 KB
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1. Introduction to Conditional GANs.mp4
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MP4
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13.7 MB
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1. Introduction to Conditional GANs.srt
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SRT
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6.5 KB
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1. Introduction to DC-GANs.mp4
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MP4
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33.8 MB
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1. Introduction to DC-GANs.srt
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SRT
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10 KB
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1. Introduction to GANs.mp4
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MP4
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26.6 MB
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1. Introduction to GANs.srt
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SRT
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5.8 KB
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1. Introduction to Progressive GANs.mp4
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MP4
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12 MB
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1. Introduction to Progressive GANs.srt
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SRT
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9 KB
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1. Introduction to U-NET Architecture.mp4
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MP4
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28.3 MB
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1. Introduction to U-NET Architecture.srt
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SRT
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9.7 KB
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1. LeakGAN Long Text Generation via Adversarial Training with Leaked Information.html
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HTML
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5.7 KB
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1. Notebook Versioning Notice.html
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HTML
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1.1 KB
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1.1 Arxiv Paper.html
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HTML
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102.4 B
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10. Working of Vid2Vid GAN.html
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HTML
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204.8 B
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10. [Coding Exercise] GAN Evaluation Metrics FID Score.mp4
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MP4
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120.6 MB
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10. [Coding Exercise] GAN Evaluation Metrics FID Score.srt
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SRT
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19.9 KB
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10. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.html
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HTML
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204.8 B
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11. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.mp4
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MP4
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41.9 MB
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11. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.srt
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SRT
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5.5 KB
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11. GAN Evaluation Metrics.html
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HTML
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204.8 B
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12. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.html
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HTML
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204.8 B
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13. Conclusion, Next Steps, and Future Directions.mp4
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MP4
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8.9 MB
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13. Conclusion, Next Steps, and Future Directions.srt
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3.4 KB
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14. Conclusion, Next Steps, and Future Directions.html
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HTML
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204.8 B
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2. Introduction to Conditional GANs.html
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HTML
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204.8 B
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2. Introduction to DC-GANs.html
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HTML
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204.8 B
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2. Introduction to GANs.html
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HTML
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204.8 B
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2. Introduction to Progressive GANs.html
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HTML
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204.8 B
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2. Introduction to U-NET Architecture.html
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HTML
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204.8 B
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2. MaskGAN Towards Diverse and Interactive Facial Image Manipulation.html
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HTML
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6.6 KB
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2. PyTorch Forward and Backward Propagation.mp4
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MP4
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76.5 MB
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2. PyTorch Forward and Backward Propagation.srt
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SRT
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14.6 KB
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2.1 Arxiv Paper.html
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HTML
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102.4 B
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2.1 Course Discussions Channel on Slack.html
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HTML
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102.4 B
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2.3 Slack Channel Inactive.html
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HTML
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102.4 B
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3. Implement Conditional GAN on MNIST Dataset.mp4
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MP4
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258.6 MB
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3. Implement Conditional GAN on MNIST Dataset.srt
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SRT
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36.1 KB
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3. Implement DC-GAN on UC Birds Dataset.mp4
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MP4
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151.8 MB
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3. Implement DC-GAN on UC Birds Dataset.srt
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SRT
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16.9 KB
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3. Implement Progressive GANs on Celebs Dataset.mp4
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MP4
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308.1 MB
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3. Implement Progressive GANs on Celebs Dataset.srt
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SRT
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38.3 KB
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3. MGAN Markovian Generative Adversarial Networks.html
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HTML
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3.8 KB
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3. PyTorch Forward and Backward Propagation.html
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HTML
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204.8 B
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3. Working of GAN Loss Function.mp4
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MP4
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8.7 MB
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3. Working of GAN Loss Function.srt
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SRT
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7.3 KB
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3. Working of Pix2Pix GAN and CycleGAN.mp4
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MP4
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67 MB
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3. Working of Pix2Pix GAN and CycleGAN.srt
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SRT
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13.5 KB
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3.1 Arxiv Paper.html
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HTML
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102.4 B
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4. GraphGAN Graph Representation Learning with Generative Adversarial Nets.html
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HTML
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4.9 KB
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4. Implement Conditional GAN on MNIST Dataset.html
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HTML
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204.8 B
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4. Implement DC-GAN on UC Birds Dataset.html
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HTML
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204.8 B
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4. Implement Progressive GANs on Celebs Dataset.html
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HTML
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204.8 B
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4. PyTorch Autograd Mechanism.mp4
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MP4
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31 MB
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4. PyTorch Autograd Mechanism.srt
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SRT
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9.1 KB
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4. Working of GAN Loss Function.html
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HTML
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204.8 B
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4. Working of Pix2Pix GAN and CycleGAN.html
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HTML
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204.8 B
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4.1 Arxiv Paper.html
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HTML
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102.4 B
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4.1 Revised source code for section 1.html
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HTML
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102.4 B
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5. Hints, Tips, and Tricks for GAN Training.mp4
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MP4
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8.3 MB
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5. Hints, Tips, and Tricks for GAN Training.srt
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SRT
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5.8 KB
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5. Implementing GAN Training Methodology.mp4
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MP4
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63.9 MB
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5. Implementing GAN Training Methodology.srt
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SRT
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10.1 KB
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5. PyTorch Autograd Mechanism.html
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HTML
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204.8 B
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5. Working of Multi-way Loss Function.mp4
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MP4
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25.5 MB
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5. Working of Multi-way Loss Function.srt
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SRT
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10 KB
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5. Working of Wasserstein Loss Function.mp4
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MP4
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30.7 MB
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5. Working of Wasserstein Loss Function.srt
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SRT
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12.2 KB
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5. [Coding Exercise] Hands-on Pix2Pix GAN.mp4
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MP4
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70.3 MB
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5. [Coding Exercise] Hands-on Pix2Pix GAN.srt
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SRT
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9.1 KB
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6. Hints, Tips, and Tricks for GAN Training.html
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HTML
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204.8 B
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6. Implementing GAN Training Methodology.html
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HTML
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204.8 B
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6. PyTorch Custom Loss Function.mp4
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MP4
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89.5 MB
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6. PyTorch Custom Loss Function.srt
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SRT
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19.5 KB
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6. Working of Multi-way Loss Function.html
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HTML
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204.8 B
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6. Working of Wasserstein Loss Function.html
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HTML
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204.8 B
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6. [Coding Exercise] Hands-on Pix2Pix GAN.html
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HTML
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204.8 B
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6.1 Code Errata.html
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HTML
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102.4 B
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6.2 Reading Assignment On Loss Functions for Deep Neural Networks in Classification Katarzyna Janocha, Wojciech Marian Czarnecki.html
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HTML
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102.4 B
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7. Implement Vanilla GAN on MNIST Dataset to Generate Digits.mp4
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MP4
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123.2 MB
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7. Implement Vanilla GAN on MNIST Dataset to Generate Digits.srt
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SRT
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22.2 KB
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7. Implement Wasserstein Loss Function.mp4
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MP4
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223 MB
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7. Implement Wasserstein Loss Function.srt
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SRT
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39.1 KB
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7. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.mp4
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MP4
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119.9 MB
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7. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.srt
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SRT
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14 KB
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7. PyTorch Custom Loss Function.html
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HTML
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204.8 B
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7. [Coding Exercise] Hands-on CycleGAN.mp4
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MP4
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113 MB
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7. [Coding Exercise] Hands-on CycleGAN.srt
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SRT
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15.4 KB
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8. Implement Vanilla GAN on MNIST Dataset to Generate Digits.html
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HTML
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204.8 B
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8. Implement Wasserstein Loss Function.html
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HTML
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204.8 B
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8. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.html
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HTML
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204.8 B
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8. [Coding Exercise] Hands-on CycleGAN.html
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HTML
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204.8 B
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9. Working of Vid2Vid GAN.mp4
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MP4
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23.3 MB
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9. Working of Vid2Vid GAN.srt
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SRT
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7 KB
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9. [Coding Exercise] GAN Evaluation Metrics Inception Score.mp4
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MP4
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127.9 MB
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9. [Coding Exercise] GAN Evaluation Metrics Inception Score.srt
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SRT
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20.3 KB
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9. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.mp4
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MP4
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97.9 MB
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9. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.srt
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SRT
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21.5 KB
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Bonus Resources.txt
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TXT
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409.6 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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Section 1.ipynb
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IPYNB
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39.6 KB
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Section 2 - Exercise.ipynb
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IPYNB
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24.4 KB
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Section 2.ipynb
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IPYNB
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27.6 KB
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Section 3 - Bonus - Learning Rate Decay.ipynb
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IPYNB
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30.7 KB
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Section 3 - Lecture 2 - FashionMNIST Excercise.ipynb
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IPYNB
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108.2 KB
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Section 3 - Lecture 2.ipynb
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IPYNB
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93.4 KB
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Section 3 - Lecture 4.ipynb
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IPYNB
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45 KB
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Section 4 - Lecture 2.ipynb
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IPYNB
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3.1 MB
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Section 4 - Lecture 4.ipynb
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IPYNB
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403.3 KB
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Section 5 - Lecture 2.ipynb
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IPYNB
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4.6 MB
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Section_3_Lecture_5_GP_WGAN.ipynb
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IPYNB
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89.9 KB
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Section_6_CycleGAN.ipynb
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IPYNB
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261 KB
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Section_6_Pix2Pix_GAN.ipynb
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IPYNB
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225 KB
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[Coding_Exercise]_GAN_Evaluation_Metrics_FID_Score.ipynb
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IPYNB
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65.9 KB
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[Coding_Exercise]_GAN_Evaluation_Metrics_Inception_Score.ipynb
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IPYNB
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37.6 KB
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