BSDA5002 · Knowledge Base
Genai Foundations Notes
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Concepts
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Facts
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Procedures
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140 - Generative Models Overview- Taxonomy, Likelihood, and Fundamentals
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141 - Generative Adversarial Networks- Theory and Implementation
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142 - Variational Autoencoders- ELBO, Reparameterization, and KL Divergence
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143 - Diffusion Models- DDPM, Forward Process, and Reverse Denoising
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144 - Denoising Diffusion Implicit Models- Accelerated Sampling
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145 - Autoregressive Models- PixelCNN, PixelRNN, and Sequential Generation
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146 - Conditional Generation and Classifier-Free Guidance
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147 - Evaluation Metrics for Generative Models- FID, IS, Precision-Recall, and Diversity
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148 - Information Theory- Entropy, KL Divergence, Mutual Information, and Cross-Entropy
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149 - Math Foundations- SVD, Eigendecomposition, Matrix Calculus, and ELBO Derivation
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