Generative AI Foundations in Python
A comprehensive guide to understanding and implementing Generative AI and Large Language Models (LLMs). This course covers the transition from theoretical foundations to practical Python-based development, including GANs, diffusion models, transformers, fine-tuning, and production deployment.
Course Overview
Content Summary
A comprehensive guide to understanding and implementing Generative AI and Large Language Models (LLMs). This course covers the transition from theoretical foundations to practical Python-based development, including GANs, diffusion models, transformers, fine-tuning, and production deployment.
Master the core principles and practical applications of modern LLMs and generative techniques with Python.
Author: Carlos Rodriguez
Acknowledgments: Special thanks to the author's wife, Jill, his parents, and the technical reviewers Morgan Boyce, Eric Rui, and Samira Shaikh (Foreword author).
Learning Objectives
- Distinguish between classical (discriminative) machine learning paradigms and generative AI models.
- Identify the foundational architectures of Generative AI, including GANs, Diffusion models, and Transformers.
- Explain the role and evolution of Large Language Models (LLMs) within the broader artificial intelligence landscape.
- Distinguish the unique features and architectural paradigms of GANs, diffusers, and transformers.
- Analyze the advancements and limitations of each model type, including specific issues like mode collapse or sampling speed.
- Implement a Stable Diffusion pipeline and evaluate generated outputs using CLIP-based logits and probabilities.
- Trace the evolution of NLP from early count-based methods and RNNs to modern Distributed Representations and Transfer Learning.
- Explain the technical mechanics of Multi-head Attention (MHA), Self-attention, Masking, and the Feed-Forward Network (FFN).
- Implement a complete Transformer model architecture, including data tokenization, positional encoding, and training/inference functions.
- Map prototyping features (e.g., Google Colab) to production-ready environments using Docker and VS Code.