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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
Languages
Concepts
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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

Vector

The vector x (lowercase, bold, by convention; equivalently, Vector) can be thought of as a point in n-dimensional space. Conventionally, we mean column-vector when we say vector. The transpose of a column vector is a row vector with the same number of elements, arranged in a single row.

Vector

Vector

Scalar product of vectors

Also known as the dot product, the scalar product is defined for two vectors of equal length. The result of the operation is a scalar value and is obtained by summing over the products of the corresponding elements of the vectors. Thus, given vectors x and y:

Scalar product of vectors

The dot product xTy is given as:

Scalar product of vectors
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