Math Foundations of Generative AI Models for ML Engineers

Equip yourself with the essential mathematical tools and techniques to master generative AI models, from GANs and VAEs to diffusion models and normalizing flows.
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Linear Algebra for Generative Models

Unit 1: Matrix Operations for Generative Models

Unit 2: Eigenvalue Decomposition

Unit 3: Singular Value Decomposition (SVD)

Unit 4: PCA-Based Generative Models

Probability, Statistics, and Information Theory in Generative Modeling

Unit 1: Probability Distributions in Generative Models

Unit 2: Statistical Concepts for Training Generative Models

Unit 3: Information Theory for Generative Model Evaluation

Calculus-Based Optimization for Training Generative Models

Unit 1: Gradient Descent Fundamentals

Unit 2: Advanced Optimization Algorithms

Unit 3: Backpropagation in Generative Models

Unit 4: Addressing Training Challenges

Stochastic Processes and Advanced Generative Models

Unit 1: Markov Chains for Generative Modeling

Unit 2: Diffusion Models: Forward and Reverse Processes

Unit 3: Transformers for Generative Tasks