Generative AI Course
About This Course
Course Description
A comprehensive course exploring the fundamentals and applications of Generative AI. Students will learn the theoretical foundations, architecture designs, and practical implementations of various generative models, with hands-on experience in building and deploying generative AI applications.
Course Objectives
By the end of this course, students will be able to:
- Understand core generative AI concepts and architectures
- Implement various types of generative models
- Fine-tune and deploy large language models
- Create practical generative AI applications
- Evaluate and address ethical considerations
- Deploy generative AI solutions responsibly
Prerequisites
- Python programming proficiency
- Basic understanding of machine learning concepts
- Linear algebra and probability basics
- Computer with minimum 16GB RAM and GPU access
Assessment Structure
- Weekly assignments (30%)
- Model implementation projects (25%)
- Ethics case studies (15%)
- Final project (30%)
Course Materials
- Jupyter notebooks
- Code repositories
- Research papers
- Case studies
- Ethics guidelines
Technical Requirements
- Python 3.8+
- PyTorch
- GPU access (local or cloud)
- Jupyter Lab/Notebook
- Required libraries
Support Resources
- Live coding sessions
- Office hours
- Discussion forums
- Research paper reviews
- Industry expert sessions
Additional Tools
- Cloud GPU platforms
- Model hosting services
- Experiment tracking tools
- Visualization tools
- Deployment platforms
Curriculum
12 Lessons
Module 1: Foundations of Generative AI (2 weeks)
Week 1: Introduction to Generative AI
Week 2: Deep Learning Essentials
Module 2: Core Generative Models (3 weeks)
Module 3: Advanced Models and Applications (4 weeks)
Module 4: Production and Ethics (3 weeks)
Your Instructors
AI
See more
Free
