Model - Tag - Zhiversehttps://yao-chih.netlify.app/en/tags/model/Model - Tag - ZhiverseHugo -- gohugo.ioenyaochihh@gmail.com (Josh)yaochihh@gmail.com (Josh)ZhiverseFrom Gradient Descent to Stochastic Gradient Descenthttps://yao-chih.netlify.app/en/from-gradient-descent-to-stochastic-gradient-descent/Sat, 30 Aug 2025 10:54:32 +0800Authorhttps://yao-chih.netlify.app/en/from-gradient-descent-to-stochastic-gradient-descent/

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Gradient descent is an iterative method used in most modern models to help find optimal parameters. However, in today’s era of big data, the massive amount of data results in significant time costs for gradient descent. One key goal is to compute parameters quickly without sacrificing accuracy.

]]>Methods for Selecting Modelshttps://yao-chih.netlify.app/en/methods-for-selecting-models/Sun, 20 Jul 2025 11:55:51 +0800Authorhttps://yao-chih.netlify.app/en/methods-for-selecting-models/<div class="featured-image"> <img src="https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/Methods%20for%20Selecting%20Models/cover%20image.png" referrerpolicy="no-referrer"> </div>How can we evaluate and compare multiple models, and select the best one for statistical analysis or machine learning? Simple models may fail to fit the data well, while overly complex models may suffer from "overfitting", reducing their predictive performance on future data.