In this video, we will learn about Generative Adversarial Networks (GANs):
✅ The architecture of GANs: Generators and Discriminators explained
✅ How GANs create data that mimics real-world examples
✅ Key loss functions used in training GANs (e.g., Binary Cross-Entropy)

This is a conceptual guide perfect for beginners and enthusiasts who want to understand the theory behind GANs without diving into the code. If you're exploring deep learning or AI, this video is a must-watch!

📧 For inquiries or collaborations: aarohisingla1987@gmail.com

Timestamps:
00:00 – Introduction
00:59 – Different Types of GAN Networks
04:45 – GAN Architecture Explained
05:57 – What is Random Noise and How to Get It?
10:00 – What is the Generator and How Does It Work?
32:07 – How to Calculate Loss for the Generator?
37:07 – What is the Discriminator?
38:49 – Formula for Calculating Generator Loss
43:30 – How to Calculate Discriminator Loss?
50:03 – Conclusion

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