Explore the world of generative AI and diffusion models in this 55-minute podcast episode featuring ML Scientist Yuri Plotkin. Delve into Plotkin's journey from biomedical engineering to machine learning, and discover the inspiration behind his upcoming book, "The Variational Book." Learn about the evolution of diffusion models, their applications in video generation, and the complexities of Markovian vs. Non-Markovian models. Gain insights into visualizing model evolution, understanding ML architectures, and balancing AI complexity. This episode also touches on influencing LLM latent space and the optimization of algorithms, providing a comprehensive overview of cutting-edge topics in artificial intelligence and machine learning.
The Variational Book - Exploring Generative AI and Diffusion Models
Overview
Syllabus
[] Yuri's preferred coffee
[] Takeaways
[] Register for the Data Engineering for AIML Conference now!
[] Yuri's background
[] The Variational Book
[] Not including LLMs in the book
[] Diffusion models
[] Evolution within diffusion models
[] Diffusion models for video
[] Evolution and optimization of algorithms
[] Markovian
[33:06 - ] SAS Ad
[] Understanding Markovian vs Non-Markovian models
[] Visualizing model evolution
[] Models through time
[] The Variational Book inspiration
[] Influencing LLM latent space
[] Understanding ML Architectures
[] Balancing AI complexity
[] Wrap up
Taught by
MLOps.community
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