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

Concepts12

Groups

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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AllBeginnerIntermediate
โˆ‘MathIntermediate

Law of Large Numbers

The Weak Law of Large Numbers (WLLN) says that the sample average of independent, identically distributed (i.i.d.) random variables with finite mean gets close to the true mean with high probability as the sample size grows.

#law of large numbers#weak law#sample mean+12
โˆ‘MathAdvanced

Evidence Lower Bound (ELBO)

The Evidence Lower Bound (ELBO) is a tractable lower bound on the log evidence log p(x) used to perform approximate Bayesian inference.

#elbo
Advanced
Filtering by:
#monte carlo
#variational inference
#vae
+12
โˆ‘MathIntermediate

Discount Factor & Return

The discounted return G_t sums all future rewards but down-weights distant rewards by powers of a discount factor ฮณ.

#discount factor#discounted return#reinforcement learning+12
โˆ‘MathAdvanced

Stochastic Differential Equations for Generation

A forward stochastic differential equation (SDE) models a state that drifts deterministically and is shaken by random Brownian noise over time.

#stochastic differential equation#diffusion model#euler maruyama+12
โˆ‘MathAdvanced

Sigma-Algebras & Measure Spaces

A ฯƒ-algebra is a collection of subsets that is closed under complements and countable unions, giving us a stable universe of sets where measure makes sense.

#sigma-algebra#measure space#measurable sets+12
โˆ‘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
โˆ‘MathBeginner

Conditional Probability

Conditional probability measures the chance of event A happening when we already know event B happened.

#conditional probability#bayes theorem#law of total probability+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#pmf#pdf+12
โˆ‘MathIntermediate

Probability Axioms & Rules

Kolmogorovโ€™s axioms define probability as a measure on events: non-negativity, normalization, and countable additivity.

#kolmogorov axioms#probability measure#sample space+12
โˆ‘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
โˆ‘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
โˆ‘MathIntermediate

Probability Fundamentals

Probability quantifies uncertainty by assigning numbers between 0 and 1 to events in a sample space.

#probability#sample space#conditional probability+12