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blog.ephorie.de | ||
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statsandr.com
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| | | | | Learn how to perform a descriptive analysis of your data in R, from simple descriptive statistics to more advanced graphics used to describe your data at hand | |
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sebastianraschka.com
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| | | | | I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs). | |
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debrouwere.org
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| | | | | [AI summary] The article discusses the limitations of traditional descriptive statistics like the mean, standard deviation, and correlation, advocating for more intuitive and robust measures. It emphasizes the importance of understanding data through alternative metrics such as medians, interquartile ranges, and percentile ranks, which are better suited for interpretation and communication. The piece also addresses the challenges of working with skewed data, outliers, and high-dimensional datasets, suggesting practical approaches like histograms and robust statistical methods. The author highlights the need for descriptive statistics to be more user-friendly and accessible, rather than being primarily focused on inferential analysis. | |
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vxlabs.com
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| | | I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time. | ||
