Generative AI Tutorial: A Complete Guide
A comprehensive guide to Generative AI — from foundational concepts like GANs, transformers, and autoencoders to real-world applications in text, image, audio, and video generation. Each chapter includes explanations, code examples, diagrams, and hands-on exercises.
Table of Contents
- 1. Introduction to Generative AI Basics, Evolution, ML Foundations, Generative vs Discriminative Models
- 2. Evolution of Generative Models History, Key Breakthroughs, Milestones, Timeline
- 3. ML Foundations for GenAI Supervised, Unsupervised, Reinforcement Learning, Transfer Learning, Fine-tuning
- 4. Types of Generative Models VAEs, GANs, Autoregressive Models, Flow-Based, Diffusion Models, Transformers
- 5. Probability & Estimation Distributions, Density Functions, MLE, KL Divergence, Bayesian Inference
- 6. Generative Adversarial Networks GAN Architecture, Conditional GAN, StyleGAN, CycleGAN, Training, Applications
- 7. Transformers in Generative AI Attention, Multi-Head Attention, Positional Encoding, GPT Architecture
- 8. Autoencoders & VAEs Concepts, Types, Implementation, Variational Autoencoders, Applications
- 9. ChatGPT & LLMs Overview, Training, RLHF, Prompt Engineering, Capabilities, Limitations
- 10. Industry Applications Manufacturing, Software, Cybersecurity, Marketing, Healthcare, Media, Music, Movies
- 11. Practice & Resources Exercises, Study Plan, Bootcamp, Certification, Ethics & Limitations
- 12. References Glossary, Cheatsheet, Useful Resources