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ddarmon.github.io | ||
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tachy.org
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| | | | | Notes on p-values. | |
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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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cyclostationary.blog
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| | | | | Our toolkit expands to include basic probability theory. | |
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jaketae.github.io
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| | | Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is... | ||
