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

Concepts532

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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AllBeginnerIntermediateAdvanced
โˆ‘MathIntermediate

Huber Loss & Smooth L1

Huber loss behaves like mean squared error (quadratic) for small residuals and like mean absolute error (linear) for large residuals, making it both stable and robust.

#huber loss#smooth l1#robust regression+12
โˆ‘MathBeginner

Mean Squared Error (MSE)

Mean Squared Error (MSE) measures the average of the squared differences between true values and predictions, punishing larger mistakes more strongly.

#mean squared error
23456
#mse
#sse
+11
โˆ‘MathIntermediate

Cross-Entropy Loss

Cross-entropy loss measures how well predicted probabilities match the true labels by penalizing confident wrong predictions heavily.

#cross-entropy#binary cross-entropy#softmax+11
โš™๏ธAlgorithmAdvanced

Wake-Sleep Algorithm

The Wakeโ€“Sleep algorithm trains a pair of models: a generative model that explains how data are produced and a recognition model that guesses hidden causes from observed data.

#wake-sleep#helmholtz machine#generative model+12
๐Ÿ“šTheoryAdvanced

Variational Dropout & Bayesian Deep Learning

Dropout can be interpreted as variational inference in a Bayesian neural network, where applying random masks approximates sampling from a posterior over weights.

#bayesian neural networks#variational inference#dropout+12
โš™๏ธAlgorithmIntermediate

Expectation Maximization (EM)

Expectation Maximization (EM) is an iterative algorithm to estimate parameters when some variables are hidden or unobserved.

#expectation maximization#em algorithm#e-step+12
๐Ÿ“šTheoryAdvanced

Normalizing Flow Variational Inference

Normalizing-flow variational inference enriches the variational family by transforming a simple base distribution through a sequence of invertible, differentiable mappings.

#normalizing flows#variational inference#elbo+12
โš™๏ธAlgorithmAdvanced

Stochastic Variational Inference

Stochastic Variational Inference (SVI) scales variational inference to large datasets by taking noisy but unbiased gradient steps using minibatches.

#stochastic variational inference#elbo#variational inference+12
๐Ÿ“šTheoryIntermediate

Mean Field Variational Family

Mean field variational family assumes the joint posterior over latent variables factorizes into independent pieces q(z) = โˆ q_i(z_i).

#mean field#variational inference#elbo+11
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#variational inference#vae+12
๐Ÿ“šTheoryIntermediate

Variational Inference

Variational Inference (VI) turns Bayesian inference into an optimization problem by choosing a simple family q(z) to approximate an intractable posterior p(z|x).

#variational inference#elbo#kl divergence+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