• Data Scientists and Machine Learning Practitioners
• Software Developers
• Computer Science Students
• AI Enthusiasts and Hobbyists
• Professionals in Creative Industries
• Entrepreneurs and Innovators

• Basic understanding of machine learning concepts.
• Proficiency in a programming language (Python is recommended).

Course Outline:

Session 1: Introduction to Generative AI

• What is Generative AI?
• Applications of Generative AI in various fields.
• Overview of generative models (e.g., GANs, VAEs).

Session 2: Generative Adversarial Networks (GANs)

• Understanding GAN architecture (generator and discriminator).
• Training GANs to generate images.
• Variations of GANs (e.g., DCGAN, CycleGAN).
• Hands-on coding exercise: Building and training a GAN.

Session 3: Variational Autoencoders (VAEs)

• Understanding the autoencoder architecture.
• Introduction to VAEs and how they work.
• Training VAEs to generate data.
• Hands-on coding exercise: Building and training a VAE.

Session 4: Creative Applications and Future Trends

• Real-world applications of generative models (e.g., image generation, text generation).
• Ethical considerations in Generative AI.
• Future trends and advancements in Generative AI.
• Q&A and discussion

*Additional Material that can be Included on Set Up Page

Setup / Installation expected

• A computer with Python installed.
• Jupyter Notebook or another code editor.
• Internet access for downloading code examples and resources.

Recommended Reading:

• “Generative Deep Learning” by David Foster
• Relevant research papers and articles in the field of Generative AI.