GAN Prime: Understanding and Implementing Generative Adversarial Networks

The course aims to provide participants with a comprehensive understanding of Generative Adversarial Networks (GANs) and equip them with the knowledge and skills to implement GANs in various applications. Participants will gain hands-on experience through practical exercises and projects.

Live Course

Live Class: Saturday, 02 Dec

Duration: 10 Hours

Enrolled: 0

Offered by: infyni

Live Course
$62 60% off


About Course

 Unlock the world of Generative Adversarial Networks (GANs) in this intensive 10-hour course designed for participants eager to delve into the cutting-edge realm of artificial intelligence. GANs, the revolutionary model for generating synthetic data, are reshaping industries from art to healthcare. This course provides a holistic understanding of GANs, blending theory with practical implementation, empowering participants to harness the power of GANs in their projects.

This course is jam-packed with a ton of info for anyone who is interested to learn about Generative Adversarial Networks. We take a multifaceted teaching approach where we learn GAN fundamentals from three different perspectives:

o Intuition

o Math

o Code

Course Offerings

  • Instructor-led interactive classes
  • Clarify your doubts during class
  • Access recordings of the class
  • Attend on mobile or tablet
  • Live projects to practice
  • Case studies to learn from
  • Lifetime mentorship support
  • Industry specific curriculum
  • Certificate of completion
  • Employability opportunity
  • Topics
  • Instructor (1)
  • Explore the origins and evolution of GANs.
  • Understand the fundamental components: Generator and Discriminator.
  • Grasp the basic architecture and workflow of GANs.

  • Dive into the mathematical underpinnings of GANs.
  • Unpack loss functions and optimization strategies.
  • Tackle training dynamics and common challenges.
  • Survey a spectrum of GAN variations, including Conditional GANs and Style GAN.
  • Explore applications like Cycle GAN and Pix2Pix.
  • Witness the diverse applications of GANs, from image synthesis to data augmentation.
  • Explore GANs in healthcare, finance, and other industries.
  • Implement advanced GAN architectures (DCGAN, WGAN).
  • Master transfer learning with pre-trained GAN models.
  • Evaluate generated content with industry-standard metrics.