Course Structure for Mastering Generative AI

Prerequisites

Before diving into Generative AI, it is essential to have a solid foundation in the following areas:

1. Mathematics
   • Linear Algebra
   • Calculus
   • Probability and Statistics
2. Programming Skills
   • Proficiency in Python
   • Familiarity with libraries such as NumPy, Pandas, TensorFlow, and PyTorch
3. Machine Learning Basics
   • Understanding supervised and unsupervised learning
   • Familiarity with regression and classification models

Course Outline

1. Introduction to Generative AI

• 1.1 Definition and Overview
  • What is Generative AI?
  • Differences between traditional AI and Generative AI
• 1.2 Applications of Generative AI
  • Text generation (e.g., ChatGPT)
  • Image generation (e.g., DALL-E, Midjourney)
  • Audio and video generation
• 1.3 Ethical Considerations
  • Responsible AI usage
  • Bias and fairness in generative models

2. Programming Fundamentals

• 2.1 Python for Data Science
  • Basic syntax and data structures
  • Libraries: NumPy, Pandas
• 2.2 Data Visualization Techniques
  • Matplotlib, Seaborn for visualizing data distributions

3. Machine Learning Foundations

• 3.1 Supervised Learning Techniques
  • Linear Regression, Decision Trees, k-NN
• 3.2 Unsupervised Learning Techniques
  • Clustering (K-means, Hierarchical)
• 3.3 Model Evaluation Metrics
  • Accuracy, Precision, Recall, F1 Score

4. Deep Learning Essentials

• 4.1 Neural Networks Basics
  • Structure of a neural network
  • Activation functions (ReLU, Sigmoid)
• 4.2 Advanced Architectures
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Transformers

5. Generative Models Overview

• 5.1 Variational Autoencoders (VAEs)
  • Architecture and applications
• 5.2 Generative Adversarial Networks (GANs)
  • Structure of GANs: Generator vs Discriminator
  • Training challenges and solutions
• 5.3 Diffusion Models
  • Understanding diffusion processes in generation

6. Practical Applications of Generative AI

• 6.1 Text Generation Techniques
  • Working with Large Language Models (LLMs)
• 6.2 Image Generation Techniques
  • Style Transfer and Image Synthesis
• 6.3 Audio and Video Generation Techniques

7. Advanced Topics in Generative AI

• 7.1 Knowledge Distillation and Model Compression
• 7.2 Fine-tuning Pre-trained Models
• 7.3 Multi-modal Generative Models

8. Capstone Project

• Develop a unified generative AI application that integrates:
  • Text generation
  • Image creation
  • Voice synthesis
• Present findings and practical applications of the project.

Learning Resources

To enhance understanding throughout the course, utilize the following resources:

• Online courses from platforms like Coursera or Udacity.
• Research papers on recent advancements in generative models.
• Books focusing on machine learning and deep learning principles.

This structured approach will guide learners through the complexities of Generative AI, ensuring a comprehensive grasp of both theoretical concepts and practical applications necessary for mastery in this field.