Generative AI : Probabilistic Perspective

Mastering Generative AI with a Probability Theory Foundation

Dive deep into the theoretical world of Generative AI with this comprehensive course. This program provides a robust mathematical foundation, empowering you to understand the core principles driving cutting-edge AI.

• Probability Theory Fundamentals: Begin with a solid grounding in probability theory, building an intuitive grasp of essential probabilistic concepts crucial for generative modeling.
• Generative AI Architectures Explained: Systematically explore key Generative AI architectures, understanding their inner workings from first principles. Learn about:
  • Variational Autoencoders (VAEs): A powerful technique for learning latent representations.
  • Score-Based Models: A modern approach to density estimation.
  • Energy-Based Models: Understanding energy functions in generative modeling.
  • Normalizing Flows: Transforming complex distributions into simpler ones.
  • Autoregressive Models (Transformers): Explore the architecture behind Large Language Models (LLMs) and their impact.
  • Generative Adversarial Networks (GANs): Discover the revolutionary approach to AI-generated media.
• Theoretical Rigor: Unlike many coding-focused courses, this program prioritizes theoretical understanding. Gain a deep appreciation for the mathematical underpinnings of generative AI models.
• Conceptual Understanding: Develop a profound conceptual understanding of how different generative models function, enabling critical analysis and independent AI research.
• Real-World Applications: Connect theoretical concepts to real-world applications, appreciating the significance of generative AI in modern AI advancements and future innovations.

This course offers a novel introduction to Gen AI from a probability perspective, equipping you with the knowledge to become a proficient and insightful AI practitioner.