Fundamentals of Generative AI
Description
Core concepts, terminology, and foundational principles of generative artificial intelligence, including different types of AI models and their applications.
Learning Objectives
Learners will understand the fundamental concepts of generative AI, differentiate between various AI paradigms including supervised, unsupervised, and reinforcement learning, recognize different types of generative models such as Large Language Models and diffusion models, and comprehend how these models learn and generate content.
Topics (8)
Comprehensive understanding of different machine learning approaches including supervised learning with labeled data, unsupervised learning for pattern discovery, and reinforcement learning for decision-making scenarios.
Deep dive into transformer architecture, attention mechanisms, and how LLMs are trained to understand and generate human-like text across various domains.
This topic covers the basic definitions of AI and ML, historical context, and how they form the foundation for generative AI technologies.
Exploration of various generative model architectures, their strengths, weaknesses, and appropriate use cases for different content generation tasks.
Comprehensive overview of data formats and structures used to train generative models, including text, images, audio, and multimodal data handling.
Understanding of training processes, loss functions, optimization techniques, and how models develop the ability to generate new content similar to training data.
Exploration of real-world applications including content creation, code generation, customer service, healthcare, education, and creative industries.
Critical analysis of current limitations including hallucinations, bias, computational requirements, and ethical considerations in generative AI deployment.