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Concepts8

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📐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

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🔷All∑Math⚙️Algo🗂️DS📚Theory

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📚TheoryIntermediate

Group Convolution

Group convolution combines two functions defined on a group by summing over products aligned by the group operation, generalizing the usual circular convolution on integers modulo n.

#group convolution#finite group#circular convolution+10
⚙️AlgorithmIntermediate

Temporal Difference Learning

Temporal Difference (TD) Learning updates value estimates by bootstrapping from the next state's current estimate, enabling fast, online learning.

#temporal difference learning
Advanced
Filtering by:
#random walk
#td(0)
#sarsa
+12
∑MathIntermediate

Random Walks on Graphs

A random walk on a graph moves from a node to one of its neighbors chosen uniformly at random at each step.

#random walk#transition matrix#stationary distribution+11
∑MathIntermediate

Markov Chains

A Markov chain models a system that moves between states where the next step depends only on the current state, not the past.

#markov chain#transition matrix#stationary distribution+11
📚TheoryIntermediate

Markov Chain Theory

A Markov chain is a random process where the next state depends only on the current state, not the full history.

#markov chain#transition matrix#stationary distribution+12
📚TheoryIntermediate

Spectral Graph Theory

Spectral graph theory studies graphs by looking at eigenvalues and eigenvectors of matrices like the adjacency matrix A and Laplacians L and L_norm.

#spectral graph theory#laplacian#normalized laplacian+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

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