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 liorpachter.wordpress.com
What is principal component analysis? | Bits of DNA
| | poissonisfish.com
Principal Component Analysis in R - poissonisfish
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| | Principal component analysis (PCA) is routinelyemployed on a wide range of problems. From the detection of outliers topredictive modeling, PCAhas the ability of projecting the observations described by $latex p &s=1$ variables into few orthogonal components defined at where thedata 'stretch' the most, rendering a simplified overview. PCA is particularly powerful in dealing with multicollinearity...
| | alexhwilliams.info
Everything you did and didn't know about PCA ยท Its Neuronal
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| | [AI summary] A technical blog post explaining the mathematical foundations of Principal Component Analysis (PCA), its various generalizations like Sparse and Non-negative Matrix Factorization, and practical considerations for choosing components and handling missing data.
| | evelinag.com
Christmas Carol and other eigenvectors
3.2 parsecs away

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| | This is day 15 of the F# Advent Calendar. Do you know who wrote the classic Christmas story, 'A Christmas Carol'? Are you sure it was Charles Dickens? In this blog post, I'm using Principal Component Analysis to compare and visualise similarity between classic works of literature.
| | blog.fastforwardlabs.com
"Hello world" in Keras (or, Scikit-learn versus Keras)
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| This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.