GenAI Technical Deep Dive for College Students

A comprehensive course designed to equip college students with the technical skills and knowledge necessary to understand, implement, and evaluate Generative AI models.
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Introduction to Generative AI

Unit 1: Understanding Generative AI

Unit 2: GenAI Applications and Model Comparison

Generative Adversarial Networks (GANs)

Unit 1: GAN Architecture and Training

Unit 2: GAN Implementation and Challenges

Variational Autoencoders (VAEs)

Unit 1: Understanding VAE Architecture and Theory

Unit 2: VAE Implementation and Comparison

Diffusion Models

Unit 1: Understanding Diffusion Models

Unit 2: Implementing and Evaluating Diffusion Models

Introduction to Large Language Models (LLMs)

Unit 1: Understanding LLMs and Transformers

Unit 2: LLM Training and Types

GPT Models: Architecture and Applications

Unit 1: GPT Architecture and Training

Unit 2: Fine-Tuning and Applications

BERT Models: Architecture and Applications

Unit 1: BERT Architecture and Training

Unit 2: BERT Applications and Fine-Tuning

Implementing GenAI Models with Python and Cloud Platforms

Unit 1: Setting Up Your GenAI Environment

Unit 2: GenAI on Cloud Platforms

Evaluating GenAI Model Performance

Unit 1: Metrics and Bias Detection

Unit 2: Implementation and Interpretation

Advanced GenAI Applications and Ethical Considerations

Unit 1: Advanced GenAI Applications

Unit 2: Ethical Considerations and Responsible Deployment