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How I Study AI - Learn AI Papers & Lectures the Easy Way

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๐Ÿ“Linear Algebra15๐Ÿ“ˆCalculus & Differentiation10๐ŸŽฏOptimization14๐ŸŽฒProbability Theory12๐Ÿ“ŠStatistics for ML9๐Ÿ“กInformation Theory10๐Ÿ”บConvex Optimization7๐Ÿ”ขNumerical Methods6๐Ÿ•ธGraph Theory for Deep Learning6๐Ÿ”ตTopology for ML5๐ŸŒDifferential Geometry6โˆžMeasure Theory & Functional Analysis6๐ŸŽฐRandom Matrix Theory5๐ŸŒŠFourier Analysis & Signal Processing9๐ŸŽฐSampling & Monte Carlo Methods10๐Ÿง Deep Learning Theory12๐Ÿ›ก๏ธRegularization Theory11๐Ÿ‘๏ธAttention & Transformer Theory10๐ŸŽจGenerative Model Theory11๐Ÿ”ฎRepresentation Learning10๐ŸŽฎReinforcement Learning Mathematics9๐Ÿ”„Variational Methods8๐Ÿ“‰Loss Functions & Objectives10โฑ๏ธSequence & Temporal Models8๐Ÿ’ŽGeometric Deep Learning8

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๐Ÿ”ทAllโˆ‘Mathโš™๏ธAlgo๐Ÿ—‚๏ธDS๐Ÿ“šTheory

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โˆ‘MathIntermediate

Wasserstein Distance & Optimal Transport

Wasserstein distance (Earth Moverโ€™s Distance) measures how much โ€œworkโ€ is needed to transform one probability distribution into another by moving mass with minimal total cost.

#wasserstein distance#earth mover's distance#optimal transport+12
โˆ‘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
Advanced
Filtering by:
#cdf
#pmf
#pdf
+12
๐Ÿ“šTheoryAdvanced

Optimal Transport Theory

Optimal Transport (OT) formalizes the cheapest way to move one probability distribution into another given a cost to move mass.

#optimal transport#wasserstein distance#kantorovich+12
๐Ÿ“šTheoryIntermediate

Probability Distributions

Probability distributions describe how random outcomes are spread across possible values and let us compute probabilities, expectations, and uncertainties.

#probability distributions#pmf#pdf+12