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Genai Foundations
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Generative Models Overview: Taxonomy, Likelihood, and Fundamentals
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Generative Adversarial Networks: Theory and Implementation
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Variational Autoencoders: ELBO, Reparameterization, and KL Divergence
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Diffusion Models: DDPM, Forward Process, and Reverse Denoising
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Denoising Diffusion Implicit Models: Accelerated Sampling
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Autoregressive Models: PixelCNN, PixelRNN, and Sequential Generation
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Conditional Generation and Classifier-Free Guidance
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Evaluation Metrics for Generative Models: FID, IS, Precision-Recall, and Diversity
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Information Theory: Entropy, KL Divergence, Mutual Information, and Cross-Entropy
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Math Foundations: SVD, Eigendecomposition, Matrix Calculus, and ELBO Derivation
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BSDA5002 — Generative AI Foundations
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