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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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🔷All∑Math⚙️Algo🗂️DS📚Theory

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∑MathIntermediate

Taylor Series & Approximation

Taylor series approximate a complicated function near a point by a simple polynomial built from its derivatives.

#taylor series#taylor polynomial#maclaurin series+12
∑MathIntermediate

Gradient & Directional Derivatives

The gradient \(\nabla f\) points in the direction of steepest increase of a scalar field and its length equals the maximum rate of increase.

#gradient
Advanced
Filtering by:
#central difference
#directional derivative
#partial derivative
+12
∑MathIntermediate

Partial Derivatives

Partial derivatives measure how a multivariable function changes when you wiggle just one input while keeping the others fixed.

#partial derivatives#gradient#jacobian+12
∑MathIntermediate

Derivatives & Differentiation Rules

Derivatives measure how fast a function changes, and rules like the product, quotient, and chain rule let us differentiate complex expressions efficiently.

#derivative#product rule#quotient rule+11