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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (13 chapters)
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10
A. Linear Algebra
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12
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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