🎓How I Study AIHISA
📖Read
📄Papers📰Blogs🎬Courses
💡Learn
🛤️Paths📚Topics💡Concepts🎴Shorts
🎯Practice
📝Daily Log🎯Prompts🧠Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts5

Groups

📐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

Category

🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginnerIntermediate
📚TheoryIntermediate

Mean Field Variational Family

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

#mean field#variational inference#elbo+11
📚TheoryAdvanced

Maximum Entropy Principle

The Maximum Entropy Principle picks the probability distribution with the greatest uncertainty (entropy) that still satisfies the facts you know (constraints).

#maximum entropy principle
Advanced
Filtering by:
#exponential family
#jaynes
#exponential family
+12
∑MathIntermediate

Sufficient Statistics

A sufficient statistic compresses all information in the sample about a parameter into a lower-dimensional summary without losing inferential power.

#sufficient statistic#fisher neyman factorization#exponential family+12
∑MathIntermediate

Exponential Family Distributions

Exponential family distributions express many common probability models in a single template p(x|η) = h(x) exp(η^T T(x) − A(η)).

#exponential family#natural parameter#sufficient statistics+12
📚TheoryIntermediate

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