# pseudocode

Published articles for pseudocode.

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## The Perceptron, and All the Things it Can't Perceive

DevFeed: [The Perceptron, and All the Things it Can't Perceive](<https://devfeed.tech/articles/the-perceptron-and-all-the-things-it-can-t-perceive-40238.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2011/08/11/the-perceptron-and-all-the-things-it-cant-perceive/>)

Published: 2011-08-11T13:16:32Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>)

Tags: [classification](<https://devfeed.tech/tags/classification.md>), [linear-algebra](<https://devfeed.tech/tags/linear-algebra.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [perceptron](<https://devfeed.tech/tags/perceptron.md>), [programming](<https://devfeed.tech/tags/programming.md>), [pseudocode](<https://devfeed.tech/tags/pseudocode.md>)

### AI overview

This tutorial introduces the machine learning classification problem and explains the perceptron as a model that separates labeled data with a line or hyperplane.

### Source excerpt

This post assumes some basic familiarity with Euclidean geometry and linear algebra. Though we do not assume so much knowledge as is contained in our primer on inner product spaces, we will be working with the real Euclidean inner product. For the purpose of this post, it suffices to know about the "dot product" of two vectors. The General Problem One of the main problems in machine learning is to classify data.

## Graph Coloring, or Proof by Crayon

DevFeed: [Graph Coloring, or Proof by Crayon](<https://devfeed.tech/articles/graph-coloring-or-proof-by-crayon-40226.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2011/07/14/graph-coloring-or-proof-by-crayon/>)

Published: 2011-07-14T22:24:13Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Graphs](<https://devfeed.tech/topics/graphs.md>), [graph theory](<https://devfeed.tech/topics/graph-theory.md>), [color](<https://devfeed.tech/topics/color.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [color](<https://devfeed.tech/tags/color.md>), [graph](<https://devfeed.tech/tags/graph.md>), [graph-coloring](<https://devfeed.tech/tags/graph-coloring.md>), [graph-theory](<https://devfeed.tech/tags/graph-theory.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [map](<https://devfeed.tech/tags/map.md>), [maps](<https://devfeed.tech/tags/maps.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [primer](<https://devfeed.tech/tags/primer.md>), [pseudocode](<https://devfeed.tech/tags/pseudocode.md>)

### AI overview

This tutorial introduces graph coloring through map-coloring examples, including the provinces of Costa Rica and the arrondissements of Paris. It models adjacent regions as vertices connected by edges, defines a valid graph coloring, and introduces the chromatic number as the smallest number of colors needed.

### Source excerpt

How many colors are required to color the provinces of Costa Rica? A common visual aid for maps is to color the regions of the map differently, so that no two regions which share a border also share a color. For example, to the right is a map of the provinces of Costa Rica (where the author is presently spending his vacation). It is colored with eight different colors, one for each province.

## Google's Page Rank--The Final Product

DevFeed: [Google's Page Rank--The Final Product](<https://devfeed.tech/articles/google-s-page-rank-the-final-product-40206.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2011/06/20/googles-page-rank-the-final-product/>)

Published: 2011-06-20T22:07:41Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Google](<https://devfeed.tech/topics/google.md>), [Matrix](<https://devfeed.tech/topics/matrix-org.md>), [math](<https://devfeed.tech/topics/math.md>), [Web](<https://devfeed.tech/topics/web.md>)

Tags: [computational-complexity](<https://devfeed.tech/tags/computational-complexity.md>), [eigenvalues](<https://devfeed.tech/tags/eigenvalues.md>), [eigenvectors](<https://devfeed.tech/tags/eigenvectors.md>), [google](<https://devfeed.tech/tags/google.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [matrix](<https://devfeed.tech/tags/matrix.md>), [page-rank](<https://devfeed.tech/tags/page-rank.md>), [probability-theory](<https://devfeed.tech/tags/probability-theory.md>), [programming](<https://devfeed.tech/tags/programming.md>), [pseudocode](<https://devfeed.tech/tags/pseudocode.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [search-engine](<https://devfeed.tech/tags/search-engine.md>), [web](<https://devfeed.tech/tags/web.md>)

### AI overview

This article explains how PageRank addresses dangling nodes and non-unique rankings. It introduces positive column-stochastic matrices and the Perron-Frobenius Theorem, then uses random web surfing as a probability-based intuition for webpage importance.

### Source excerpt

Dangling Nodes and Non-Uniqueness Recall where we left off last time. Given a web $ W$ with no dangling nodes, the link matrix for $ W$ has 1 as an eigenvalue, and if the corresponding eigenspace has dimension 1, then any associated eigenvector gives a ranking of the pages in $ W$ which is consistent with our goals. The first problem is that if there is a dangling node, our link matrix has a column of all zeros, and is no longer column-stochastic.

## Well Orderings and Search

DevFeed: [Well Orderings and Search](<https://devfeed.tech/articles/well-orderings-and-search-40202.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2011/06/14/well-orderings-and-search/>)

Published: 2011-06-14T11:18:04Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [Sorting](<https://devfeed.tech/topics/sorting.md>), [ordering](<https://devfeed.tech/topics/ordering.md>), [implementation](<https://devfeed.tech/topics/implementation.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [axiom-of-choice](<https://devfeed.tech/tags/axiom-of-choice.md>), [binary-search](<https://devfeed.tech/tags/binary-search.md>), [mathematica](<https://devfeed.tech/tags/mathematica.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [ordering](<https://devfeed.tech/tags/ordering.md>), [pseudocode](<https://devfeed.tech/tags/pseudocode.md>), [recursion](<https://devfeed.tech/tags/recursion.md>), [sorting](<https://devfeed.tech/tags/sorting.md>), [well-ordering](<https://devfeed.tech/tags/well-ordering.md>)

### AI overview

This tutorial explains binary search on sorted lists, including its recursive structure and O(log n) runtime. It then introduces strict total and well orders to explain why sorting and comparison work.

### Source excerpt

Binary Search Binary search is perhaps the first and most basic nontrivial algorithm a student learns. For the mathematicians out there, binary search is a fast procedure to determine whether a sorted list contains a particular element. Here is a pseudocode implementation: # Binary Search: # Given a list L, sorted via the total order <, and a sought # element x, return true iff L contains x. function binarySearch(L, x, <): # base case if(length(L) == 1): return L[0] == x middleIndex = floor(length(L) / 2) if (L[middleIndex] == x): return true # inductive step, with ellipsis notation meaning slices of L # from the beginning and to the end, respectively if (x < L[middleIndex]): return binarySort(L[.