This course provides a comprehensive introduction to Generative AI, covering its theoretical foundations, practical applications, and ethical considerations. Learners will explore various generative models, their training methods, and real-world use cases while also addressing the challenges and risks associated with AI-generated content.

This section introduces the core concepts of Generative AI, explaining how it differs from traditional AI and its significance in modern technology. Learners will explore the history of generative models, from early rule-based systems to advanced deep learning architectures like GANs and Transformers. By the end of this section, students will have a clear understanding of the key models and their real-world impact.

This section delves into the mathematical and computational principles that power Generative AI. Topics include probability distributions, Bayesian inference, and neural networks, which form the backbone of generative models. Students will gain insight into how deep learning techniques enable AI systems to create realistic text, images, and other forms of synthetic data.

In this section, students will learn how generative models are trained, optimized, and evaluated. Key topics include data collection, preprocessing techniques, loss functions, and performance metrics used to assess AI-generated content. Hands-on insights into training models like GANs and VAEs will help learners understand the practical challenges and trade-offs in model development.

This section explores key challenges in Generative AI, including ethical concerns, legal issues, and environmental impact. It also highlights emerging regulations and future trends shaping responsible AI development.

Generative AI

Challenge: Build Simple VAE

Generative AI

Challenge: Build Simple VAE