<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Math for AI — The Latent Daily</title><description>Linear algebra, probability and optimization for practitioners.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/math-for-ai.xml" rel="self" type="application/rss+xml"/><item><title>The math behind AI: what you actually need to know</title><link>https://thelatentdaily.com/guides/math-you-need-for-ai/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/math-you-need-for-ai/</guid><description>Linear algebra, calculus, probability and statistics: which parts of math matter for understanding and building AI, explained with intuition first.</description><pubDate>Sun, 18 Oct 2026 05:28:00 GMT</pubDate><category>Math for AI</category><category>math</category><category>learning</category><category>machine-learning</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>Gradient descent explained with one simple example</title><link>https://thelatentdaily.com/guides/gradient-descent-explained-simple-example/</link><guid isPermaLink="true">https://thelatentdaily.com/guides/gradient-descent-explained-simple-example/</guid><description>The algorithm that trains almost every AI model, explained with a hiker in the fog and a few lines of Python: gradients, learning rates and why training can fail.</description><pubDate>Sun, 18 Oct 2026 05:27:00 GMT</pubDate><category>Math for AI</category><category>math</category><category>gradient-descent</category><category>machine-learning</category><category>python</category><author>dario_admin@resolvia-crm.tech</author></item><item><title>The Adam optimizer explained: the algorithm that trains most neural networks</title><link>https://thelatentdaily.com/research/adam-optimizer-paper-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/adam-optimizer-paper-explained/</guid><description>Adam combined momentum with per-parameter learning rates and became the default way to train deep learning models. How it works, why it won and its known pitfalls.</description><pubDate>Sun, 18 Oct 2026 05:22:00 GMT</pubDate><category>Machine Learning</category><category>research</category><category>optimization</category><category>deep-learning</category><category>adam</category><author>dario_admin@resolvia-crm.tech</author></item></channel></rss>