15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
- Data Scientists
- Machine Learning Engineers
- AI Engineers and Developers
- Deep Learning Specialists
- Computer Vision Engineers
- Data Engineers
- AI Researchers and Research Scientists
- Python Developers working with AI/ML
- Image Processing and Computer Vision Professionals
- Generative AI Professionals
- Synthetic Data Specialists
- MLOps and AI Platform Engineers
Session Objectives
- Understand the fundamentals of Generative Adversarial Networks (GANs).
- Master the architecture and training of basic GANs.
- Utilize conditional GANs for controlled image generation.
- Implement style transfer techniques for artistic image creation.
- Design and build GANs for synthetic data generation.
- Optimize GAN models for stability and image quality.
- Troubleshoot and address common GAN training challenges.
- Implement model evaluation and validation techniques for GANs.
- Integrate GAN models into real-world applications.
- Understand how to handle mode collapse and other training issues.
- Explore advanced GAN architectures (e.g., StyleGAN, CycleGAN).
- Apply real world use cases for GANs in various domains.
- Leverage deep learning libraries for efficient GAN implementation.
About the Course
This advanced training course provides participants with a practical understanding of Generative Adversarial Networks (GANs) and their applications in synthetic data generation, image synthesis, and generative AI. Participants will explore how GANs use competing generator and discriminator networks to learn data distributions and produce realistic synthetic outputs.The course covers fundamental GAN architectures, model training, loss functions, optimization techniques, image generation, data augmentation, synthetic dataset creation, and techniques for improving generation quality. Participants will examine popular GAN variants such as DCGANs, Conditional GANs, Wasserstein GANs (WGANs), and StyleGAN, as well as the challenges associated with training instability, mode collapse, overfitting, and evaluation of synthetic outputs.Through practical exercises and real-world applications, participants will learn how GANs can be used for computer vision, healthcare research, financial modeling, data augmentation, media production, simulation, and privacy-aware synthetic data generation. The course also addresses ethical considerations, responsible use, synthetic-data risks, and emerging developments in generative AI.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of Generative Adversarial Networks (GANs).
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Workshop 1.2:
Overview of GAN architectures and training principles.
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Workshop 1.3:
Setting up a GANs development environment.
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Workshop 1.4:
Introduction to GANs libraries and tools.
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Workshop 1.5:
Best practices for GANs.
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Workshop 2.1:
Implementing basic GAN architectures (DCGAN).
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Workshop 2.2:
Utilizing generator and discriminator networks.
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Workshop 2.3:
Designing and building simple image generation models.
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Workshop 2.4:
Optimizing GAN training for image quality.
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Workshop 2.5:
Best practices for basic GANs.
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Workshop 3.1:
Implementing conditional GANs for controlled generation.
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Workshop 3.2:
Utilizing class labels and conditioning inputs.
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Workshop 3.3:
Designing and building cGANs for specific tasks.
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Workshop 3.4:
Optimizing cGANs for conditional generation.
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Workshop 3.5:
Best practices for cGANs.
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Workshop 4.1:
Implementing style transfer using GANs.
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Workshop 4.2:
Utilizing perceptual loss and style loss.
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Workshop 4.3:
Designing and building artistic image generation models.
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Workshop 4.4:
Optimizing style transfer for creative applications.
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Workshop 4.5:
Best practices for style transfer.
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Workshop 5.1:
Designing GANs for synthetic data generation.
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Workshop 5.2:
Implementing GANs for tabular and time-series data.
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Workshop 5.3:
Utilizing GANs for data augmentation and privacy.
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Workshop 5.4:
Optimizing GANs for data synthesis tasks.
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Workshop 5.5:
Best practices for synthetic data.
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Workshop 6.1:
Optimizing GAN models for stability and image quality.
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Workshop 6.2:
Utilizing advanced loss functions and regularization techniques.
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Workshop 6.3:
Implementing hyperparameter tuning for GANs.
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Workshop 6.4:
Designing scalable GAN training pipelines.
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Workshop 6.5:
Best practices for GAN optimization.
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Workshop 7.1:
Debugging common GAN training issues.
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Workshop 7.2:
Analyzing mode collapse and instability.
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Workshop 7.3:
Utilizing troubleshooting techniques for model improvement.
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Workshop 7.4:
Resolving common GAN challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing evaluation metrics for GANs (FID, IS).
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Workshop 8.2:
Utilizing visual inspection and qualitative analysis.
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Workshop 8.3:
Designing and building model validation pipelines.
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Workshop 8.4:
Optimizing evaluation strategies for GANs.
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Workshop 8.5:
Best practices for model evaluation.
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Workshop 9.1:
Integrating GAN models into real-world applications.
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Workshop 9.2:
Utilizing APIs and deployment tools for GANs.
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Workshop 9.3:
Implementing real-time generative models.
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Workshop 9.4:
Optimizing models for deployment environments.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding and addressing mode collapse.
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Workshop 10.2:
Utilizing techniques for stabilizing GAN training.
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Workshop 10.3:
Designing and building robust training pipelines.
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Workshop 10.4:
Optimizing training strategies for stability.
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Workshop 10.5:
Best practices for training stability.
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Workshop 11.1:
Implementing StyleGAN for high-resolution image generation.
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Workshop 11.2:
Utilizing CycleGAN for image-to-image translation.
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Workshop 11.3:
Designing and building advanced GAN models.
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Workshop 11.4:
Optimizing advanced architectures for specific tasks.
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Workshop 11.5:
Best practices for advanced architectures.
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Workshop 12.1:
Implementing GANs for image inpainting and super-resolution.
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Workshop 12.2:
Utilizing GANs for medical image synthesis.
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Workshop 12.3:
Implementing GANs for virtual reality and gaming.
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Workshop 12.4:
Utilizing GANs for fashion and design.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing TensorFlow and PyTorch for GAN implementation.
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Workshop 13.2:
Implementing GAN models with Keras and other libraries.
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Workshop 13.3:
Designing and building GAN pipelines with libraries.
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Workshop 13.4:
Optimizing library usage for efficient implementation.
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Workshop 13.5:
Best practices for library implementation.
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Workshop 14.1:
Implementing interpretability techniques for GANs.
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Workshop 14.2:
Utilizing visualization tools for understanding generated data.
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Workshop 14.3:
Designing and building interpretable GAN models.
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Workshop 14.4:
Optimizing model transparency.
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Workshop 14.5:
Best practices for model interpretability.
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Workshop 15.1:
Emerging trends in generative adversarial networks.
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Workshop 15.2:
Utilizing transformer-based GANs.
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Workshop 15.3:
Implementing GANs for 3D and video generation.
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Workshop 15.4:
Best practices for future GAN applications.
