Foundations of Generative AI

This unit surveys the historical foundations of computing and artificial intelligence and builds conceptual fluency in machine learning, deep learning, and generative AI core technologies. Learners will examine neural network architectures and transformer principles and gain an applied understanding of how deep learning workloads are implemented on AWS to prepare for hands-on model development and deployment in subsequent units.

Learning Objectives

• Analyze historical milestones from mechanical reasoning through the rise of deep learning to explain how each era contributed to modern generative AI
• Differentiate between machine learning, deep learning, and generative AI technologies by comparing key concepts, common neural network architectures, and their typical use cases
• Demonstrate the end-to-end machine learning/deep learning workflow and describe the AWS services and resources commonly used to run and scale deep learning workloads
• Evaluate transformer architecture and generative model capabilities and limitations, synthesizing key takeaways to guide responsible design and implementation in later units