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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
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-* Retrieval Augmented Generation (RAG)
-  
-* Ethics and Safety in GenAI
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-* 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