<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Tensor Almanac</title><link>https://tensoralmanac.com</link><description>Understand the machine.</description><item><title>When NumPy broadcasting gives you the wrong loss</title><link>https://tensoralmanac.com/guides/numpy-broadcasting-shape-bugs/</link><guid>https://tensoralmanac.com/guides/numpy-broadcasting-shape-bugs/</guid><description>A shape-compatible operation can still compare the wrong values. Diagnose a silent pairwise-error bug before it reaches your metrics.</description></item><item><title>Split first, fit second: a preprocessing leakage check</title><link>https://tensoralmanac.com/guides/prevent-preprocessing-leakage/</link><guid>https://tensoralmanac.com/guides/prevent-preprocessing-leakage/</guid><description>Use a small scaler diagnostic to see whether held-out data is influencing training, then put the boundary inside a scikit-learn pipeline.</description></item><item><title>Estimate KV-cache memory before choosing a context length</title><link>https://tensoralmanac.com/guides/estimate-kv-cache-memory/</link><guid>https://tensoralmanac.com/guides/estimate-kv-cache-memory/</guid><description>Calculate the dense cache payload from layers, KV heads, token count and dtype, and see why the answer is not a GPU-fit guarantee.</description></item></channel></rss>