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Concepts7

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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

Multi-Task Loss Balancing

Multi-task loss balancing aims to automatically set each task’s weight so that no single loss dominates training.

#multi-task learning#uncertainty weighting#homoscedastic uncertainty+12
📚TheoryIntermediate

Dropout

Dropout randomly turns off (zeros) some neurons during training to prevent the network from memorizing the training data.

#dropout
Advanced
Filtering by:
#variance
#inverted dropout
#bernoulli mask
+12
∑MathIntermediate

Expectation, Variance & Moments

Expectation is the long-run average value of a random variable and acts like the balance point of its distribution.

#expectation#variance#moments+12
∑MathIntermediate

Random Variables & Distributions

A random variable maps uncertain outcomes to numbers and is described by a distribution that assigns likelihoods to values or ranges.

#random variable#pmf#pdf+12
📚TheoryIntermediate

Probability Theory

Probability theory formalizes uncertainty using a sample space, events, and a probability measure that obeys clear axioms.

#probability measure#random variable#expectation+12
∑MathIntermediate

Variance and Covariance

Variance measures how spread out a random variable is around its mean, while covariance measures how two variables move together.

#variance#covariance#standard deviation+12
∑MathIntermediate

Expected Value

Expected value is the long-run average outcome of a random variable if you could repeat the experiment many times.

#expected value#linearity of expectation#indicator variables+12