Udemy - Introduction to Generative Adversarial Networks with PyTorch

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[ DevCourseWeb.com ] Udemy - Introduction to Generative Adversarial Networks with PyTorch
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Course Agenda
    • 1. Course Agenda.mp4 (91.5 MB)
    • 1. Course Agenda.srt (9.0 KB)
    2. Introduction to PyTorch for GANs
    • 1. Notebook Versioning Notice.html (1.1 KB)
    • 2. PyTorch Forward and Backward Propagation.mp4 (76.5 MB)
    • 2. PyTorch Forward and Backward Propagation.srt (14.6 KB)
    • 2.1 Course Discussions Channel on Slack.html (0.1 KB)
    • 2.3 Slack Channel Inactive.html (0.1 KB)
    • 3. PyTorch Forward and Backward Propagation.html (0.2 KB)
    • 4. PyTorch Autograd Mechanism.mp4 (31.0 MB)
    • 4. PyTorch Autograd Mechanism.srt (9.1 KB)
    • 4.1 Revised source code for section 1.html (0.1 KB)
    • 5. PyTorch Autograd Mechanism.html (0.2 KB)
    • 6. PyTorch Custom Loss Function.mp4 (89.5 MB)
    • 6. PyTorch Custom Loss Function.srt (19.5 KB)
    • 6.1 Code Errata.html (0.1 KB)
    • 6.2 Reading Assignment On Loss Functions for Deep Neural Networks in Classification Katarzyna Janocha, Wojciech Marian Czarnecki.html (0.1 KB)
    • 7. PyTorch Custom Loss Function.html (0.2 KB)
    • Section 1.ipynb (39.6 KB)
    3. Generate Handwritten Digits with Vanilla GAN
    • 1. Introduction to GANs.mp4 (26.6 MB)
    • 1. Introduction to GANs.srt (5.8 KB)
    • 10. [Coding Exercise] GAN Evaluation Metrics FID Score.mp4 (120.6 MB)
    • 10. [Coding Exercise] GAN Evaluation Metrics FID Score.srt (19.9 KB)
    • 11. GAN Evaluation Metrics.html (0.2 KB)
    • 2. Introduction to GANs.html (0.2 KB)
    • 3. Working of GAN Loss Function.mp4 (8.7 MB)
    • 3. Working of GAN Loss Function.srt (7.3 KB)
    • 4. Working of GAN Loss Function.html (0.2 KB)
    • 5. Implementing GAN Training Methodology.mp4 (63.9 MB)
    • 5. Implementing GAN Training Methodology.srt (10.1 KB)
    • 6. Implementing GAN Training Methodology.html (0.2 KB)
    • 7. Implement Vanilla GAN on MNIST Dataset to Generate Digits.mp4 (123.2 MB)
    • 7. Implement Vanilla GAN on MNIST Dataset to Generate Digits.srt (22.2 KB)
    • 8. Implement Vanilla GAN on MNIST Dataset to Generate Digits.html (0.2 KB)
    • 9. [Coding Exercise] GAN Evaluation Metrics Inception Score.mp4 (127.9 MB)
    • 9. [Coding Exercise] GAN Evaluation Metrics Inception Score.srt (20.3 KB)
    • Section 2 - Exercise.ipynb (24.4 KB)
    • Section 2.ipynb (27.6 KB)
    • [Coding_Exercise]_GAN_Evaluation_Metrics_FID_Score.ipynb (65.9 KB)
    • [Coding_Exercise]_GAN_Evaluation_Metrics_Inception_Score.ipynb (37.6 KB)
    4. Generate Specific Digits with Conditional GAN
    • 1. Introduction to Conditional GANs.mp4 (13.7 MB)
    • 1. Introduction to Conditional GANs.srt (6.5 KB)
    • 10. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.html (0.2 KB)
    • 2. Introduction to Conditional GANs.html (0.2 KB)
    • 3. Implement Conditional GAN on MNIST Dataset.mp4 (258.6 MB)
    • 3. Implement Conditional GAN on MNIST Dataset.srt (36.1 KB)
    • 4. Implement Conditional GAN on MNIST Dataset.html (0.2 KB)
    • 5. Working of Wasserstein Loss Function.mp4 (30.7 MB)
    • 5. Working of Wasserstein Loss Function.srt (12.2 KB)
    • 6. Working of Wasserstein Loss Function.html (0.2 KB)
    • 7. Implement Wasserstein Loss Function.mp4 (223.0 MB)
    • 7. Implement Wasserstein Loss Function.srt (39.1 KB)
    • 8. Implement Wasserstein Loss Function.html (0.2 KB)
    • 9. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.mp4 (97.9 MB)
    • 9. [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN.srt (21.5 KB)
    • Section 3 - Bonus - Learning Rate Decay.ipynb (30.7 KB)
    • Section 3 - Lecture 2 - FashionMNIST Excercise.ipynb (108.2 KB)
    • Section 3 - Lecture 2.ipynb (93.4 KB)
    • Section 3 - Lecture 4.ipynb (45.0 KB)
    • Section_3_Lecture_5_GP_WGAN.ipynb (89.9 KB)
    5. Diving Deeper with a Deep Convolutional GAN
    • 1. Introduction to DC-GANs.mp4 (33.8 MB)
    • 1. Introduction to DC-GANs.srt (10.0 KB)
    • 2. Introduction to DC-GANs.html (0.2 KB)
    • 3. Implement DC-GAN on UC Birds Dataset.mp4 (151.8 MB)
    • 3. Implement DC-GAN on UC Birds Dataset.srt (16.9 KB)
    • 4. Implement DC-GAN on UC Birds Dataset.html (0.2 KB)
    • 5. Working of Multi-way Loss Function.mp4 (25.5 MB)
    • 5. Working of Multi-way Loss Function.srt (10.0 KB)
    • 6. Working of Multi-way Loss Function.html (0.2 KB)
    • 7. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.mp4 (119.9 MB)
    • 7. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.srt (14.0 KB)
    • 8. Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset.html (0.2 KB)
    • Section 4 - Lecture 2.ipynb (3.1 MB)
    • Section 4 - Lecture 4.ipynb (403.3 KB)
    6. Generate Realistic Human Faces with Progressive GAN
    • 1. Introduction to Progressive GANs.mp4 (12.0 MB)
    • 1. Introduction to Progressive GANs.srt (9.0 KB)
    • 2. Introduction to Progressive GANs.html (0.2 KB)
    • 3. Implement Progressive GANs on Celebs Dataset.mp4 (308.1 MB)
    • 3. Implement Progressive GANs on Celebs Dataset.srt (38.3 KB)
    • 4. Implement Progressive GANs on Celebs Dataset.html (0.2 KB)
    • 5. Hints, Tips, and Tricks for GAN Training.mp4 (8.3 MB)
    • 5. Hints, Tips, and Tricks for GAN Training.srt (5.8 KB)
    • 6. Hints, Tips, and Tricks for GAN Training.html (0.2 KB)
    • Section 5 - Lecture 2.ipynb (4.6 MB)
    7. Generate Videos from Other Videos
    • 1. Introduction to U-NET Architecture.mp4 (28.3 MB)
    • 1. Introduction to U-NET Architecture.srt (9.7 KB)
    • 10. Working of Vid2Vid GAN.html (0.2 KB)
    • 11. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.mp4 (41.9 MB)
    • 11. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.srt (5.5 KB)
    • 12. Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset.html (0.2 KB)
    • 13. Conclusion, Next Steps, and Future Directions.mp4 (8.9 MB)
    • 13. Conclusion, Next Steps, and Future Directions.srt (3.4 KB)
    • 14. Conclusion, Next Steps, and Future Directions.html (0.2 KB)

    • Description

      Introduction to Generative Adversarial Networks with PyTorch



      https://DevCourseWeb.com

      MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
      Genre: eLearning | Language: English + srt | Duration: 34 lectures (5h 54m) | Size: 1.96 GB
      A comprehensive course on GANs including state of the art methods, recent techniques, and step-by-step hands-on projects
      What you'll learn:
      How Generative Adversarial Networks work internally
      How to implement state of the art GANs techniques and methods using PyTorch
      How to improve the training stability of GANs

      Requirements
      Familiarity with Python Programming
      Familiarity with Deep Learning Concepts

      Description
      Master the basic building blocks of modern generative adversarial networks with a unique course that reviews the most recent research papers in GANs and at the same time gives the learner a very detailed hands-on experience in the topic. Start by learning the very basics of how GANs work and incrementally learn more cleverly crafted techniques that enhance your models from the basic GANs towards the more advanced Progressive Growing of GANs. On the journey, you shall learn a fair amount of deep learning concepts with an adequate discussion of the mathematics behind the modern models.

      Who this course is for
      Data scientists willing to take their skills to the next level in the area of GANs
      Research / Postgraduate Students willing to get a comprehensive overview of recent advancement made in the area of GANs
      Deep Learning practitioners willing to apply GANs at work in production environments
      Enthusiasts willing to stay up to date on GANs research and development
      Deep learning beginners willing to master the building blocks of modern GANs



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Udemy - Introduction to Generative Adversarial Networks with PyTorch

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2.3 GB
seeders:2
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Udemy - Introduction to Generative Adversarial Networks with PyTorch

Torrent hash: 44FC39CA00FD57506C443F04AAA16A3D6A8F8645