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Machine Learning for the Web

Machine Learning for the Web

By : Steve Essinger, Isoni
4.5 (27)
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Machine Learning for the Web

Machine Learning for the Web

4.5 (27)
By: Steve Essinger, Isoni

Overview of this book

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python’s impressive Django framework and will find out how to build a modern simple web app with machine learning features.
Table of Contents (10 chapters)
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9
Index

Singular value decomposition

This method is based on a theorem that states that a matrix X d x N can be decomposed as follows:

Singular value decomposition

Here:

  • U is a d x d unitary matrix
  • ∑ is a d x N diagonal matrix where the diagonal entries si are called singular values
  • V is an N x N unitary matrix

In our case, X can be composed by the feature's vectors Singular value decomposition, where each Singular value decomposition is a column. We can reduce the number of dimensions of each feature vector d, approximating the singular value decomposition. In practice, we consider only the largest singular values Singular value decomposition so that:

Singular value decomposition

t represents the dimension of the new reduced space where the feature vectors are projected. A vector x(i) is transformed in the new space using the following formula:

Singular value decomposition

This means that the matrix Singular value decomposition (not Singular value decomposition) represents the feature vectors in the t dimensional space.

Note that it is possible to show that this method is very similar to the PCA; in fact, the scikit-learn library uses SVD to implement PCA.

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