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ABOUT THE COURSE:
This course provides an in-depth exploration of deep generative models, including their probabilistic foundations and learning algorithms. Students will learn about various types of deep generative models such as variational autoencoders, generative adversarial networks, autoregressive models, Diffusion Models and Large Language Models. The course will cover both theoretical foundations and practical implementations of these models using popular frameworks like PyTorch. Students will gain hands-on experience through lectures and assignments, allowing them to explore deep generative models across various AI tasks.
INTENDED AUDIENCE: Academics and Industry
PREREQUISITES: Probability, Course in Machine Learning
INDUSTRY SUPPORT: All ML Companies
This course provides an in-depth exploration of deep generative models, including their probabilistic foundations and learning algorithms. Students will learn about various types of deep generative models such as variational autoencoders, generative adversarial networks, autoregressive models, Diffusion Models and Large Language Models. The course will cover both theoretical foundations and practical implementations of these models using popular frameworks like PyTorch. Students will gain hands-on experience through lectures and assignments, allowing them to explore deep generative models across various AI tasks.
INTENDED AUDIENCE: Academics and Industry
PREREQUISITES: Probability, Course in Machine Learning
INDUSTRY SUPPORT: All ML Companies
