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janakiev.com | ||
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www.oranlooney.com
In the previous article in this series we distinguished between two kinds of unsupervised learning (cluster analysis and dimensionality reduction) and discussed the former in some detail. In this installment we turn our attention to the later. In dimensionality reduction we seek a function \(f : \mathbb{R}^n \mapsto \mathbb{R}^m\) where \(n\) is the dimension of the original data \(\mathbf{X}\) and \(m\) is less than or equal to \(n\). That is, we want to map some high dimensional space into some lower dimensional space. |
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algobeans.com
You are exploring the nutritional content of food. How can food items be differentiated? How might they be classified? PCA derives underlying variables that help you slice your data for these insights. |
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nlml.github.io
This is the first post in the In Raw Numpy series. This series is an attempt to provide readers (and myself) with an understanding of some of the most frequently-used machine learning methods by going through the math and intuition, and implementing it using just python and numpy. |
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www.onlandscape.co.uk
[AI summary] The article discusses the use of cookies on the On Landscape website, explaining their purpose and the user's ability to opt-out. |
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