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The Nature of Code

natureofcode.com
Sam Marder Sam Marder
1 year ago
Beautiful free online "book" (HTML book) of physics stuff in programming
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Home - ROME - Confluence

Home - ROME - Confluence

rometools.jira.com
Sam Marder Sam Marder
1 year ago
Excellent open-source Java project for reading different feeds (RSS and Atom)
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Bracket - Wikipedia, the free encyclopedia

en.wikipedia.org
Sam Marder Sam Marder
1 year ago
Explanation of different brackets () [] {} <>

() Parentheses
[] brackets or square brackets
{} curly braces or braces
<> angle brackets or inequality signs
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Parse any date in Java - Stack Overflow

stackoverflow.com
Sam Marder Sam Marder
1 year ago
How to parse a date in Java without knowing the format.
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Sorting Custom Types in Java

www.javabeat.net
Sam Marder Sam Marder
1 year ago
Create custom comparator in java.
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Cosine similarity - Wikipedia, the free encyclopedia

en.wikipedia.org
Sam Marder Sam Marder
1 year ago
Explains the basic (read: ultra complex) process of data mining words.
This is often used to compare documents in text mining. In addition, it is used to measure cohesion within clusters in the field of data mining.[1]
Central idea of this page.
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What algorithm is used to determine the most important words in an English sentence?

What algorithm is used to determine the most important words in an English sentence?

www.quora.com
Sam Marder Sam Marder
1 year ago
Sort words by frequency. Includes link for excluding common words in the English language.
I would say TF-IDF

TF stands for Term Frequency. Given an English term t and a document d, TF(t,d) represents how many t appears in d

DF stands for Document Frequency. Given an English term t and a list of documents D (d1~dn in D), DF(t,D) represents how many documents di are there such that t appears in di at least once

IDF is Inverted DF. Basically the bigger DF value, the smaller IDF value is

But sometimes, raw frequency is not what we want. A document with 10 appearances of a term is more relevant than another document with only one, but not 10 times as relevant. In this case, we use Log-Frequency Weighting
The more often a term appears, the more important it is
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Excellent link for words that should be excluded (unimportant words)
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