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All Blog Posts Tagged 'pca' (2)

Naive Principal Component Analysis in R

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set the those yourself,…

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Added by Pablo Bernabeu on September 6, 2017 at 1:30pm — No Comments

The Data-Driven Weekly #1.2

Last week witnessed a number of exciting announcements from the big data and machine learning space. What it shows is that there are still lots of problems to solve in 1) working with/deriving insights from big data, 2) integrating insights into business processes.



TensorFlow

Probably the biggest (data) headline was that Google open sourced TensorFlow, their graph-based…

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Added by Brian Rowe on November 17, 2015 at 6:02am — No Comments

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