GANs with Keras

GANs with Keras

Generative Adversarial Networks (GANs) are a special branch of AI where machines learn to generate new, lifelike content. They involve a “game” between two models: the generator, which creates artificial data, and the discriminator, which judges whether that data is real or fake. Through continuous learning, the generator becomes skilled at producing realistic outcomes, making GANs powerful for innovation in visuals, sound, and data simulation.

By using Keras, developers can focus on the logic of GANs instead of spending too much time on low-level programming. Keras offers simple APIs that make it easier to design these models, run experiments, and achieve high-quality results. This pairing of GANs and Keras opens the door for creative AI projects, from realistic art to advanced simulations.

Who should take the Exam?

This exam is ideal for:

• Machine Learning Engineers
• AI Researchers
• Data Scientists
• Deep Learning Specialists
• Computer Vision Engineers
• Applied AI Developers
• Research Scholars in AI

Skills Required

• Python programming fundamentals
• Basic knowledge of deep learning and neural networks
• Familiarity with Keras or TensorFlow framework
• Understanding of training data and preprocessing

Course Outline

Domain 1 - Introduction to GANs

Domain 2 - Keras Basics for GANs

Domain 3 - Building the Generator

Domain 4 - Building the Discriminator

Domain 5 - Training GANs

Domain 6 - Advanced GAN Models

Domain 7 - Practical Applications

Domain 8 - Future Trends in GANs