🎓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

Concepts172

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
⚙️AlgorithmAdvanced

Knuth Optimization

Knuth Optimization speeds up a class of interval dynamic programming (DP) from O(n^3) to O(n^2) by exploiting the monotonicity of optimal split points.

#knuth optimization#interval dp#quadrangle inequality+12
⚙️AlgorithmAdvanced

Digit DP - Advanced States

Digit DP counts integers in a range by scanning digits from most significant to least while maintaining compact state information.

#digit dp
1011121314
Advanced
#tight flag
#leading zeros
+12
⚙️AlgorithmAdvanced

DP with Probability

DP with probability models how chance flows between states over time by repeatedly redistributing mass according to transition probabilities.

#markov chain#probability dp#absorbing state+12
⚙️AlgorithmAdvanced

Sum over Subsets (SOS) DP

Sum over Subsets (SOS) DP lets you compute F[mask] = sum of A[submask] over all submasks in O(n 2^n) instead of O(3^n).

#sos dp#subset zeta transform#mobius inversion+11
⚙️AlgorithmAdvanced

DP with Expected Value

Dynamic programming with expected value solves problems where each state transitions randomly and we seek the expected cost, time, or steps to reach a goal.

#expected value dp#linearity of expectation#indicator variables+11
∑MathAdvanced

Game Theory - Advanced Games

Sprague–Grundy (SG) theory solves impartial, normal-play, terminating games by assigning each position a nonnegative integer called its Grundy value.

#sprague-grundy#grundy number#nim-sum+12
∑MathAdvanced

Berlekamp-Massey Algorithm

Berlekamp–Massey (BM) finds the shortest linear recurrence that exactly fits a given sequence over a field (e.g., modulo a prime).

#berlekamp-massey#linear recurrence#minimal polynomial+11
∑MathAdvanced

Gaussian Elimination over GF(2)

Gaussian elimination over GF(2) is ordinary Gaussian elimination where addition and subtraction are XOR and multiplication is AND.

#gaussian elimination#gf(2)#xor basis+12
∑MathAdvanced

Linear Recurrence

A linear recurrence defines each term as a fixed linear combination of a small, fixed number of previous terms.

#linear recurrence#matrix exponentiation#kitamasa+12
⚙️AlgorithmAdvanced

Block-Cut Tree

A Block-Cut Tree decomposes an undirected graph into biconnected components (blocks) and articulation points, forming a bipartite tree.

#block-cut tree#biconnected components#articulation points+11
⚙️AlgorithmAdvanced

Hungarian Algorithm

The Hungarian algorithm solves the square assignment problem (matching n workers to n jobs) in O(n^{3}) time using a clever potential (label) function on vertices.

#hungarian algorithm#assignment problem#bipartite matching+11
⚙️AlgorithmAdvanced

General Matching - Blossom Algorithm

Edmonds' Blossom Algorithm finds a maximum matching in any undirected graph, not just bipartite ones.

#blossom algorithm#edmonds matching#general graph matching+12