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leimao.github.io | ||
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joellaity.com
The code used to generate the plots for this post can be found here. |
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gregorygundersen.com
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peterbloem.nl
[AI summary] The text provides a comprehensive overview of Principal Component Analysis (PCA), starting with its mathematical foundation and practical applications. It explains the concept of PCA as a method for dimensionality reduction by projecting data onto a lower-dimensional subspace while preserving as much variance as possible. The article covers the derivation of PCA, its connection to eigenvectors and eigenvalues, and its use in various fields such as image processing, genetics, and data analysis. It also discusses the theoretical underpinnings of PCA, including the generalization of the best approximation theorem to higher dimensions and the role of orthogonal projections. The text includes references to key works in linear algebra and statistics, ... |
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kvfrans.com
In my previous post about generative adversarial networks, I went over a simple method to training a network that could generate realistic-looking images. However, there were a couple of downsides to using a plain GAN. First, the images are generated off some arbitrary noise. If you wanted to generate a |
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