README.md

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# About this Space:
This space contains Python notebooks, datasets, and homework instructions for the course 94-844 Generative AI Lab at Heinz College.

# 94-844 Generative AI Lab at Heinz College

This course provides an in-depth exploration of generative artificial intelligence, covering both the
theoretical underpinnings of generative models and the practical application of generative AI tools.
Students will learn about the latest advancements in generative models, including Variational
Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer-based models
like GPT and BERT for text, as well as diffusion models for image generation. Emphasis is placed
on understanding model architectures, training processes, and the ethical considerations of
generative AI. Weekly labs will provide students with hands-on experience with generative AI tools
and platforms, such as OpenAI's GPTs, Llama, Stable Diffusion, and Hugging Face, and allow
students to work on exercises and projects

Some of the topics that will be covered in the course include:

* Deep Generative Models
  
* Variational Autoencoders (VAEs)
  
* Generative Adversarial Networks (GANs)
  
* Autoregressive Models and Energy-based Models
  
* Transformers and Text Generation
  
* Diffusion Models and Image Generation
  
* Pre-training and Fine-tuning
  
* Retrieval Augmented Generation (RAG)
  
* Ethics and Safety in GenAI
  
* Red Teaming GenAI Models

This course is designed for students with a background in technology, business, policy, management,
or related fields who aspire to become proficient in GenAI technologies. We will combine lectures,
case studies, hands-on labs and projects, and industry guest speakers to provide with a holistic
understanding of GenAI in today’s world