ML Pulse

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Probability

Distributions, expectation, concentration, limit theorems

Vectors, matrices, eigen-decomposition, calculus in n-dimensional space

Gradient descent, SGD, convex optimization, modern optimizers

Feedforward, activations, autoencoders, training dynamics

Attention, scaling, pretraining, modern LLM architecture

VC, PAC, Rademacher, bias-variance, generalization

MLE, Fisher info, Cramér-Rao, hypothesis testing

MDPs, Bellman equations, exploration, RLHF

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