Online/ e-LMS
Mentor Based
Moderate
4 weeks
About
This program explores the theory and applications of GANs, focusing on their two-component structure (generator and discriminator). Participants will learn how GANs work, delve into advanced variants like DCGAN and CycleGAN, and implement practical projects using GANs for real-world problems such as image synthesis and data generation.
Aim
To equip PhD scholars, researchers, and AI professionals with an in-depth understanding of Generative Adversarial Networks (GANs) and their practical applications. This course covers the fundamentals, advanced techniques, and hands-on experience in building and optimizing GANs for image generation, data augmentation, and creative AI.
Program Objectives
- Learn the theory behind GANs and how they function.
- Build and train various types of GANs for image generation and creative applications.
- Master advanced GAN techniques like DCGAN, CycleGAN, and Wasserstein GAN.
- Solve real-world problems using GANs, including data augmentation and style transfer.
- Understand and address the challenges of training GANs.
Program Structure
- Introduction to GANs
- What are GANs?
- Historical Context and Importance of GANs
- Overview of Adversarial Networks (Generator vs. Discriminator)
- GAN Architecture
- Building Blocks of GANs
- Loss Functions for GANs (Minimax Game)
- Training GANs: Key Challenges and Solutions
- Training Dynamics of GANs
- Mode Collapse and Vanishing Gradients
- Techniques to Stabilize GAN Training
- GAN Evaluation Metrics (e.g., Inception Score, FID)
- Deep Dive into Variants of GANs
- Conditional GANs (cGANs)
- Deep Convolutional GANs (DCGANs)
- Wasserstein GANs (WGANs)
- Progressive GANs
- Applications of GANs
- Image Generation
- Text-to-Image Synthesis
- Video and Audio Generation
- Advanced Topics in GANs
- CycleGANs for Image-to-Image Translation
- StyleGAN and Style Transfer
- GANs in Data Augmentation and Privacy Preservation
- Ethical Implications of GANs
- Deepfakes and Their Impact
- Ethical Considerations in Using GANs
- Mitigating Harm in GAN Applications
- GANs in Research and Industry
- Recent Developments in GAN Research
- Applications in Art, Healthcare, and Entertainment
- Deploying GAN Models in Production (Cloud/Edge)
- Hands-on GANs with PyTorch/TensorFlow
- Implementing Basic GANs in PyTorch
- Customizing and Tuning GAN Architectures
- Training GANs on Custom Datasets
Participant’s Eligibility
AI researchers, data scientists, and professionals with a background in machine learning, neural networks, or computer vision.
Program Outcomes
- Ability to build and train advanced GAN models for real-world applications.
- Proficiency in optimizing GANs for stable performance and high-quality outputs.
- Deep understanding of how GANs can be applied in creative industries and data science.
- Skills to address common GAN challenges like mode collapse and training instability.
Fee Structure
Fee: INR 10,999 USD 164
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- AI Research Scientist
- Deep Learning Engineer
- GAN Specialist
- Data Augmentation Expert
- Computer Vision Engineer
- Creative AI Developer
Job Opportunities
- AI-driven startups
- Creative industries using generative AI
- Research labs focusing on computer vision and generative models
- Data science teams for synthetic data generation
- Gaming and virtual environment companies
- Animation and media production firms
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