๐ŸŽ“How I Study AIHISA
๐Ÿ“–Read
๐Ÿ“„Papers๐Ÿ“ฐBlogs๐ŸŽฌCourses
๐Ÿ’กLearn
๐Ÿ›ค๏ธPaths๐Ÿ“šTopics๐Ÿ’กConcepts๐ŸŽดShorts
๐ŸŽฏPractice
๐Ÿ“Daily Log๐ŸŽฏPrompts๐Ÿง Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts15

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

Category

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

Level

AllBeginnerIntermediate
โˆ‘MathIntermediate

Rรฉnyi Entropy & Divergence

Rรฉnyi entropy generalizes Shannon entropy by measuring uncertainty with a tunable emphasis on common versus rare outcomes.

#renyi entropy#renyi divergence#shannon entropy+12
โˆ‘MathIntermediate

Hidden Markov Models

A Hidden Markov Model (HMM) describes sequences where you cannot see the true state directly, but you can observe outputs generated by those hidden states.

#hidden markov model
12
Advanced
Filtering by:
#log-sum-exp
#forward algorithm
#viterbi
+12
๐Ÿ“šTheoryIntermediate

Knowledge Distillation Loss

Knowledge distillation loss blends standard hard-label cross-entropy with a soft distribution match from a teacher using a temperature parameter.

#knowledge distillation#kd loss#temperature scaling+12
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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“šTheoryIntermediate

Maximum Likelihood & Generative Models

Maximum Likelihood Estimation (MLE) picks parameters that make the observed data most probable under a chosen probabilistic model.

#maximum likelihood#generative models#naive bayes+12
โˆ‘MathIntermediate

Softmax & Temperature Scaling

Softmax turns arbitrary real-valued scores (logits) into probabilities that sum to one.

#softmax#temperature scaling#logits+12
๐Ÿ“šTheoryIntermediate

Scaled Dot-Product Attention

Scaled dot-product attention scores how much each value V should contribute to a query by taking dot products with keys K, scaling by \(\sqrt{d_k}\), applying softmax, and forming a weighted sum.

#scaled dot-product attention#softmax#transformer+10
โš™๏ธAlgorithmIntermediate

Importance Sampling

Importance sampling rewrites an expectation under a hard-to-sample distribution p as an expectation under an easier distribution q, multiplied by a weight w = p/q.

#importance sampling#proposal distribution#self-normalized+12
โˆ‘MathIntermediate

Numerical Stability

Numerical stability measures how much rounding and tiny input changes can distort an algorithmโ€™s output on real computers using floating-point arithmetic.

#numerical stability#forward error#backward error+12
๐Ÿ“šTheoryIntermediate

Cross-Entropy

Cross-entropy measures how well a proposed distribution Q predicts outcomes actually generated by a true distribution P.

#cross-entropy#entropy#kl divergence+12
โˆ‘MathIntermediate

Exponential Family Distributions

Exponential family distributions express many common probability models in a single template p(x|ฮท) = h(x) exp(ฮท^T T(x) โˆ’ A(ฮท)).

#exponential family#natural parameter#sufficient statistics+12