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How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts4

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๐Ÿ“Linear Algebra15๐Ÿ“ˆCalculus & Differentiation10๐ŸŽฏOptimization14๐ŸŽฒProbability Theory12๐Ÿ“ŠStatistics for ML9๐Ÿ“กInformation Theory10๐Ÿ”บConvex Optimization7๐Ÿ”ขNumerical Methods6๐Ÿ•ธGraph Theory for Deep Learning6๐Ÿ”ตTopology for ML5๐ŸŒDifferential Geometry6โˆžMeasure Theory & Functional Analysis6๐ŸŽฐRandom Matrix Theory5๐ŸŒŠFourier Analysis & Signal Processing9๐ŸŽฐSampling & Monte Carlo Methods10๐Ÿง Deep Learning Theory12๐Ÿ›ก๏ธRegularization Theory11๐Ÿ‘๏ธAttention & Transformer Theory10๐ŸŽจGenerative Model Theory11๐Ÿ”ฎRepresentation Learning10๐ŸŽฎReinforcement Learning Mathematics9๐Ÿ”„Variational Methods8๐Ÿ“‰Loss Functions & Objectives10โฑ๏ธSequence & Temporal Models8๐Ÿ’ŽGeometric Deep Learning8

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๐Ÿ”ทAllโˆ‘Mathโš™๏ธAlgo๐Ÿ—‚๏ธDS๐Ÿ“šTheory

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๐Ÿ“šTheoryIntermediate

Topological Data Analysis (TDA)

Topological Data Analysis (TDA) studies the shape of data using tools from algebraic topology, producing summaries like Betti numbers, barcodes, and persistence diagrams.

#topological data analysis#persistent homology#vietorisโ€“rips complex+12
๐Ÿ“šTheoryIntermediate

Amortized Analysis

Amortized analysis measures the average cost per operation over a worst-case sequence, not over random inputs.

#amortized analysis
Advanced
Filtering by:
#union find
#aggregate method
#accounting method
+12
โš™๏ธAlgorithmIntermediate

Minimum Spanning Tree - Kruskal

Kruskalโ€™s algorithm builds a minimum spanning tree (MST) by sorting all edges by weight and greedily picking the next lightest edge that does not form a cycle.

#kruskal#minimum spanning tree#mst+11
๐Ÿ—‚๏ธData StructureIntermediate

Rollback DSU

Rollback DSU (Disjoint Set Union with undo) lets you union sets and later revert to any previous state in LIFO order.

#rollback dsu#dsu with undo#union find+11