Unit 1: What is Generative AI?
Unit 2: Core Concepts and Foundations
Unit 3: Mathematical Foundations
Generative AI Fundamentals for Aspiring Machine Learning Engineers
A comprehensive course covering the core principles, implementation, and applications of Generative AI, equipping aspiring machine learning engineers with the skills to create, evaluate, and deploy generative models.
Unit 1: VAE Fundamentals
Unit 2: VAE Architecture
Unit 3: VAE Loss Functions
Unit 4: VAE Implementation
Unit 5: Applications and Extensions
Unit 1: GAN Architecture and Theory
Unit 2: Implementing Basic GANs
Unit 3: Advanced GAN Architectures
Unit 4: GAN Training Challenges and Solutions
Unit 5: Applications of GANs
Unit 1: Fundamentals of Diffusion Models
Unit 2: Implementing Diffusion Models
Unit 3: Advanced Techniques and Sampling Strategies
Unit 1: Evaluating Generative AI Models
Unit 2: Transfer Learning for Generative AI
Unit 3: Conditional Generative AI
Unit 1: Data Augmentation with Generative AI
Unit 2: Ethics and Bias in Generative AI
Unit 3: Prompt Engineering Techniques
Unit 4: Generative AI Pipelines
Unit 5: Future Trends in Generative AI