November 2017
Intermediate to advanced
274 pages
6h 16m
English
Content preview from Neural Network Programming with TensorFlow
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Principal Component Analysis
Principal Component Analysis (PCA) projects the given dataset onto a lower dimensional linear space so that the variance of the projected data is maximized. PCA requires the eigenvalues and eigenvectors of the covariance matrix, which is the product where X is the data matrix.
SVD on the data matrix X is given as follows:
The following example shows PCA using SVD:
import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltimport plotly.plotly as pyimport plotly.graph_objs as goimport plotly.figure_factory as FFimport pandas as pdpath = "/neuralnetwork-programming/ch01/plots"logs = "/neuralnetwork-programming/ch01/logs" ...
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