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Concepts5

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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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⚙️AlgorithmIntermediate

Discrete Fourier Transform (DFT) & FFT

The Discrete Fourier Transform (DFT) converts a length-N sequence from the time (or spatial) domain into N complex frequency coefficients that describe how much of each sinusoid is present.

#dft#fft#cooley-tukey+12
⚙️AlgorithmAdvanced

Polynomial Operations

Fast polynomial operations treat coefficients like numbers but use FFT/NTT to multiply in O(n \log n) time instead of O(n^2).

#polynomial
Advanced
Filtering by:
#convolution
#ntt
#fft
+12
⚙️AlgorithmAdvanced

Convolution Applications

Convolution turns local pairwise combinations (like matching characters or adding two dice) into a single fast transform–multiply–inverse pipeline.

#convolution#fft#ntt+12
⚙️AlgorithmAdvanced

NTT (Number Theoretic Transform)

The Number Theoretic Transform (NTT) is an FFT-like algorithm that performs discrete convolutions exactly using modular arithmetic instead of floating-point numbers.

#ntt#number theoretic transform#polynomial multiplication+11
⚙️AlgorithmAdvanced

FFT (Fast Fourier Transform)

FFT converts a polynomial from coefficients to values at the n-th roots of unity in O(n log n) time, enabling fast multiplication via pointwise products.

#fft#polynomial multiplication#convolution+11