<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>shaijm</title><description>learning through writing</description><link>https://shaijm.com/</link><item><title>When Does a Time Series Contain Predictive Information?</title><link>https://shaijm.com/posts/autocorrelation-through-simulation/</link><guid isPermaLink="true">https://shaijm.com/posts/autocorrelation-through-simulation/</guid><description>Using simulated processes, lag plots and the autocorrelation function to distinguish dependence from spurious patterns.</description><pubDate>Fri, 23 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Decomposing Time Series: From Moving Averages to STL</title><link>https://shaijm.com/posts/time-series-decomposition-stl/</link><guid isPermaLink="true">https://shaijm.com/posts/time-series-decomposition-stl/</guid><description>Trend, seasonality and remainder components, and why robust STL is often more flexible than classical decomposition.</description><pubDate>Thu, 22 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Why Adam Works: From Gradient Descent to Adaptive Optimization</title><link>https://shaijm.com/posts/why-adam-works/</link><guid isPermaLink="true">https://shaijm.com/posts/why-adam-works/</guid><description>Deriving momentum, RMSprop and Adam, with an interactive comparison on an ill-conditioned objective.</description><pubDate>Tue, 29 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Why Do Transformers Divide Attention Scores by √d?</title><link>https://shaijm.com/posts/why-scale-attention/</link><guid isPermaLink="true">https://shaijm.com/posts/why-scale-attention/</guid><description>A variance argument for scaled dot-product attention, with an interactive softmax experiment.</description><pubDate>Mon, 12 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Word2Vec, Negative Sampling, and the Geometry of Meaning</title><link>https://shaijm.com/posts/word2vec-negative-sampling/</link><guid isPermaLink="true">https://shaijm.com/posts/word2vec-negative-sampling/</guid><description>How predicting contexts produces word embeddings, and why negative sampling changes the learning problem.</description><pubDate>Tue, 15 Sep 2026 00:00:00 GMT</pubDate></item></channel></rss>