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100 days of algorithms
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Day 97: Locally weighted regression
Day 97: Locally weighted regression
Locally weighted regression is a very powerful non-parametric model used in statistical learning.
Tomáš Bouda
Jun 29
Day 96: Floyd-Steinberg
Day 96: Floyd-Steinberg
Floyd-Steinberg dithering is a truly magical technique. It is supposed to fool your eye and brain to make you think that you see more than…
Tomáš Bouda
Jun 28
Day 95: Strongly connected components
Day 95: Strongly connected components
We say that two nodes U and V in a directed graph belong to the same strongly connected component [SCC], if there exists path from U to V…
Tomáš Bouda
Jun 27
Day 94: Earley parser
Day 94: Earley parser
Yesterday I have implemented an algorithm that directly relates to an implementation of parser for formal grammars. Today I have…
Tomáš Bouda
Jun 26
Day 93: FIRST & FOLLOW
If you plan to implement own parser for a context-free grammar, construction of FIRST and FOLLOW sets will be the first algorithm you will…
Tomáš Bouda
Jun 25
Day 92: PCA
Day 92: PCA
Principal Component Analysis [PCA] is incredibly useful when you need [among others] to visualise high-dimensional data. It’s also very…
Tomáš Bouda
Jun 24
Day 91: Variations
Day 91: Variations
Take all the natural numbers in which each digit occurs at most once and sort them in the ascending order. Given this ordering, write two…
Tomáš Bouda
Jun 23
Day 90: Simple Nim — AI
Day 90: Simple Nim — AI
I have prepared something cool today. Let’s implement a program with [a simple] artificial intelligence.
Tomáš Bouda
Jun 22
Day 89: Bipartiteness
Day 89: Bipartiteness
Graph is bipartite if the nodes can be split into two disjoint sets such that there is no edge between nodes inside the same set.
Tomáš Bouda
Jun 21
Day 88: Perlin noise
Day 88: Perlin noise
It has been 35 years since Ken Perlin has discovered a technique today called Perlin noise to generate a fixed gradient noise to achieve a…
Tomáš Bouda
Jun 20
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