Course Notes
The notes written by students and edited by instructors
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Lecture 21
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Lecture 20-LLMs from a Probabilistic Perspective 1-Implementing a GPT from Scratch
How we evolved from a transformer to a GPT
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Lecture 19: The Attention Mechanism
An in-depth exploration of attention mechanisms in neural networks, from their motivation and foundational forms (hard and soft attention) to their applications in image captioning and the Transformer architecture.
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Lecture 18 - Deep Generative Models
Introduction to Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models.
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Lecture 16 - Deep Learning from a GM Perspective
Deep learning foundations viewed through the lens of graphical models, covering perceptrons, neural networks, backpropagation, and probabilistic interpretations.
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Lecture 12 - Parameter Learning of partially observed BNs
Parameter Learning of partially observed BNs
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Lecture 05 - Undirected GMs (MRFs)
Introduction to Undirected GMs
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Lecture 04 - Conditional Independence and Directed GMs (BNs)
Review of conditional independence, and an introduction to Directed GMs (BNs)
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Lecture 03 - A Linear View of Discriminative and Generative Models
Introduction to LaTex & Distinction between Discriminative and Generative Models
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Lecture 02 - Statistics review
A statistics review, including MLE and MAP