Category:Subject:Applied mathematics/all books

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This is an internally maintained category. It should be automatically populated with all books belonging to subject Applied mathematics or to any of its descendant subjects.

Subcategories

The following 3 subcategories may be of interest, out of 3 total.

S

• Subject:Machine learning/all books‎ (3 pages)
• Subject:Mathematical physics/all books‎ (4 pages)
• Subject:Statistics/all books‎ (8 pages)

Pages in category "Subject:Applied mathematics/all books"

More recent additions	More recent modifications
Biostatistics with R Statistical Analysis: an Introduction using R Machine Translation Artificial Neural Networks Statistics Calculus of Variations Support Vector Machines OpenSSL Social Statistics High School Probability and Statistics	Handbook of Descriptive Statistics Artificial Neural Networks High School Probability and Statistics From Data to Insight An Intuitive Guide to the Concept of Entropy Arising in Various Sectors of Science Support Vector Machines Statistics Statistics Ground Zero Engineering Analysis Introduction to Mathematical Physics

The following 28 pages are in this category, out of 28 total.

A

• An Intuitive Guide to the Concept of Entropy Arising in Various Sectors of Science
• Applicable Mathematics
• Applied Mathematics
• Artificial Neural Networks

B

• Biostatistics with R

C

• Calculus of Variations
• Computational Physics
• Cryptography

E

• Engineering Analysis

F

• From Data to Insight

H

• Handbook of Descriptive Statistics
• High School Probability and Statistics

I

• Introduction to Game Theory
• Introduction to Mathematical Physics

M

• Machine Translation
• Mathematical Methods of Physics
• Mathematical Statistics
• Mathematics for Chemistry
• Modelling Theory and Practice

O

• OpenSSL
• Operations Research

P

• Parallel Spectral Numerical Methods

S

• Social Statistics
• Statistical Analysis: an Introduction using R
• Statistics
• Statistics Ground Zero
• Support Vector Machines

W

• Why, and How, Should Geologists Use Compositional Data Analysis

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