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Concepts98

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
∑MathIntermediate

Markov Decision Processes (MDP)

A Markov Decision Process (MDP) models decision-making in situations where outcomes are partly random and partly under the control of a decision maker.

#markov decision process#value iteration#policy iteration+12
∑MathIntermediate

Wasserstein Distance & Optimal Transport

Wasserstein distance (Earth Mover’s Distance) measures how much “work” is needed to transform one probability distribution into another by moving mass with minimal total cost.

#wasserstein distance
12345
Advanced
#earth mover's distance
#optimal transport
+12
∑MathIntermediate

Softmax & Temperature Scaling

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

#softmax#temperature scaling#logits+12
∑MathIntermediate

Positional Encoding Mathematics

Sinusoidal positional encoding represents each token’s position using pairs of sine and cosine waves at exponentially spaced frequencies.

#positional encoding#sinusoidal#transformer+11
∑MathIntermediate

Elastic Net Regularization

Elastic Net regularization combines L1 (Lasso) and L2 (Ridge) penalties to produce models that are both sparse and stable.

#elastic net#lasso#ridge regression+12
∑MathIntermediate

L2 Regularization (Ridge/Weight Decay)

L2 regularization (also called ridge or weight decay) adds a penalty proportional to the sum of squared weights to discourage large parameters.

#l2 regularization#ridge regression#weight decay+12
∑MathIntermediate

L1 Regularization (Lasso)

L1 regularization (Lasso) adds a penalty \(\lambda \sum_{i=1}^{p} |w_i|\) to the loss, which pushes many coefficients exactly to zero and performs feature selection.

#lasso#l1 regularization#soft-thresholding+12
∑MathIntermediate

Wavelet Transform

The wavelet transform splits a signal into “coarse” trends and “fine” details at multiple scales, like zooming in and out with a smart magnifying glass.

#wavelet transform#haar wavelet#multiresolution analysis+12
∑MathIntermediate

Convolution Theorem

The Convolution Theorem says that convolving two signals in time (or space) equals multiplying their spectra in the frequency domain.

#convolution theorem#fft#dft+12
∑MathIntermediate

Fourier Transform

The Fourier Transform converts a signal from the time domain into the frequency domain, revealing which sine and cosine waves (frequencies) make up the signal.

#fourier transform#fft#dft+12
∑MathIntermediate

Fourier Series

A Fourier series rewrites any reasonable periodic function as a weighted sum of sines and cosines (or complex exponentials).

#fourier series#harmonics#fourier coefficients+12
∑MathIntermediate

Riemannian Metrics & Geometry

A Riemannian metric assigns an inner product to each tangent space, giving you a way to measure lengths and angles on curved spaces (manifolds).

#riemannian metric#metric tensor#christoffel symbols+12