AI & ML
Gen AI - Generative Adversarial Networks
This training provides a deep dive into Generative AI and Generative Adversarial Networks (GANs), covering their architecture, training process, and real-world applications. Learn to build and optimize different GAN models, including DCGANs, cGANs, and StyleGANs, using TensorFlow and PyTorch.
What is Gen AI - Generative Adversarial Networks (GANs) Training about?
This training provides a comprehensive understanding of Generative AI and Generative Adversarial Networks (GANs)—one of the most powerful deep learning architectures for generating realistic data. Learn how GANs work, their architecture, and how to train them for applications in image synthesis, text generation, deepfake creation, and more.
Gain hands-on experience with TensorFlow and PyTorch, implementing different types of GANs, such as Vanilla GAN, Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), and StyleGAN. By the end of this course, you'll be able to build and optimize GAN models for real-world use cases.
Gain hands-on experience with TensorFlow and PyTorch, implementing different types of GANs, such as Vanilla GAN, Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), and StyleGAN. By the end of this course, you'll be able to build and optimize GAN models for real-world use cases.
What are the objectives ofGen AI - Generative Adversarial Networks Training ?
By the end of this training, you will be able to:
- Understand the fundamentals of Generative AI and its role in modern applications
- Explore the architecture of Generative Adversarial Networks (GANs), including the Generator and Discriminator
- Learn how to train GANs and overcome challenges like mode collapse and vanishing gradients
- Implement and optimize different types of GANs, such as Vanilla GANs, DCGANs, cGANs, and StyleGANs
- Work on real-world applications, including image synthesis, deepfake generation, and AI-driven creativity
- Apply transfer learning and fine-tuning for specific use cases
- Optimize GANs using advanced techniques, such as Wasserstein GAN (WGAN) and Progressive Growing of GANs
- Deploy and scale GAN models for production use
Who should take this Training?
This training program is ideal for:
- Machine Learning & Deep Learning Engineers exploring Generative AI
- Data Scientists looking to apply GANs to real-world problems
- AI Enthusiasts & Researchers interested in synthetic data generation
- Developers & Engineers building AI-powered creative applications
What are the prerequisites for Training?
- Strong understanding of Python programming
- Familiarity with Deep Learning & Neural Networks
- Experience with TensorFlow or PyTorch
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