Dropout as Shared-Parameter Bagging
March 7, 2026
A concrete way to understand dropout in deep learning: sampled subnetworks with shared weights, standing in for a much more expensive ensemble.
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Notes on the mathematical foundations, network structure, and regularization sections of Deep Learning.
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Depth Changes Scaling, Not Just Capacity
February 15, 2026
Chapter 6 separates representational capacity, parameter efficiency, and learnability—and shows why depth changes scaling, not just expressiveness.
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Math Foundations for Deep Learning
February 7, 2026
Part I of Goodfellow, Bengio, and Courville works best as shared vocabulary for later chapters, with proof details still worth supplementing.
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