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📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

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📚TheoryIntermediate

Convex Optimization

Convex optimization studies minimizing convex functions over convex sets, where every local minimum is guaranteed to be a global minimum.

#convex optimization#convex function#convex set+12
📚TheoryIntermediate

Optimization Theory

Optimization theory studies how to choose variables to minimize or maximize an objective while respecting constraints.

#optimization
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#convex optimization
#gradient descent
+12
📚TheoryIntermediate

Gradient Descent Convergence Theory

Gradient descent updates parameters by stepping opposite the gradient: x_{t+1} = x_t - \eta \nabla f(x_t).

#gradient descent#convergence rate#l-smooth+12