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Mastering Java Machine Learning

You're reading from   Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785880513
Length 556 pages
Edition 1st Edition
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Authors (2):
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Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
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Toc

Table of Contents (13) Chapters Close

Preface 1. Machine Learning Review FREE CHAPTER 2. Practical Approach to Real-World Supervised Learning 3. Unsupervised Machine Learning Techniques 4. Semi-Supervised and Active Learning 5. Real-Time Stream Machine Learning 6. Probabilistic Graph Modeling 7. Deep Learning 8. Text Mining and Natural Language Processing 9. Big Data Machine Learning – The Final Frontier A. Linear Algebra B. Probability Index

Matrix

A matrix is a two-dimensional array of numbers. Each element can be indexed by its row and column position. Thus, a 3 x 2 matrix:

Matrix

Transpose of a matrix

Swapping columns for rows in a matrix produces the transpose. Thus, the transpose of A is a 2 x 3 matrix:

Transpose of a matrix

Matrix addition

Matrix addition is defined as element-wise summation of two matrices with the same shape. Let A and B be two m x n matrices. Their sum C can be written as follows:

Ci,j = Ai,j + Bi,j

Scalar multiplication

Multiplication with a scalar produces a matrix where each element is scaled by the scalar value. Here A is multiplied by the scalar value d:

Scalar multiplication

Matrix multiplication

Two matrices A and B can be multiplied if the number of columns of A equals the number of rows of B. If A has dimensions m x n and B has dimensions n x p, then the product AB has dimensions m x p:

Matrix multiplication

Properties of matrix product

Distributivity over addition: A(B + C) = AB + AC

Associativity: A(BC) = (AB)C

Non-commutativity: AB ≠ BA

Vector dot-product is commutative...

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