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Machine Learning Theory
Study notes on core machine learning theory for PhD preparation, including statistical learning theory, representation learning, and generative modeling.
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Mathematical Foundations for Transformers
Detailed study notes on the mathematics behind Transformer models, including attention, softmax, positional encoding, optimization, and efficient token computation.
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Mathematical Foundations for Machine Learning and Computer Vision
Detailed study notes on linear algebra, probability, and optimization for machine learning and computer vision.
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Building My PhD Knowledge Base for Computer Vision
A structured roadmap of the theoretical foundations required for PhD interviews in computer vision and autonomous driving.