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

Concepts4

Groups

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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AllBeginnerIntermediate
โš™๏ธAlgorithmIntermediate

Problem Classification Patterns

Many competitive programming problems map to a small set of classic patterns; recognizing keywords and constraints lets you pick the right tool fast.

#problem classification#binary search on answer#two pointers+12
โš™๏ธAlgorithmIntermediate

Dijkstra's Algorithm

Dijkstra's algorithm finds shortest path distances from one source to all vertices when all edge weights are non-negative.

#dijkstra
Advanced
Filtering by:
#dijkstra
#shortest path
#greedy
+11
โš™๏ธAlgorithmIntermediate

Dijkstra - Variations and Applications

Dijkstraโ€™s algorithm can be adapted to track the second shortest path by keeping the best and second-best distances per vertex.

#dijkstra#second shortest path#k shortest paths+12
โš™๏ธAlgorithmIntermediate

Greedy Algorithms

Greedy algorithms build a solution step by step by always taking the best local choice available.

#greedy algorithms#activity selection#interval scheduling+12