Generative AI Course

Module 1: Introduction to Generative AI

• Definition, scope, and applications
• Key generative models (GANs, VAEs, Transformers)
• Interactive teaching and assessments.

Module 2: Fundamentals of Machine Learning

• Essentials of supervised and unsupervised learning
• Neural networks, deep learning, and natural language processing
• Dynamic teaching approaches and assessments

Module 3: Generative Models

• Concepts of latent spaces and probability distributions
• In-depth examination of autoencoders, VAEs, and GANs
• Hands-on examples with image and text data

Module 4: Prompt Engineering and Model Fine-Tuning

• Importance of prompt engineering
• Designing effective prompts and fine-tuning models
• Practical experience with tools and frameworks
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Module 5: Advanced Prompt Engineering Techniques

• Mastering control over style, tone, and content
• Strategies to reduce bias and improve fairness
• Designing complex prompts through practical exercises

Module 6: Deploying Generative Models

• Best practices for production deployment
• Overview of deployment options, tools, and frameworks
• Hands-on deployment example using FastAPI and Docker

Module 7: Introduction to GitHub Copilot

• Basics of GitHub Copilot and its architecture
• Installation, setup, and configuration
• Hands-on experience and prompt engineering for code suggestions