{"height":"190","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Ftnakamura.hatenablog.com%2Fentry%2F2017%2F02%2F15%2Fgradient-descent\" title=\"\u52fe\u914d\u964d\u4e0b\u6cd5\u3092\u5b9f\u88c5\u3057\u3066\u307f\u305f - present\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/g/griefworker/20170215/20170215145505.png","version":"1.0","categories":["C#","\u6a5f\u68b0\u5b66\u7fd2"],"provider_url":"https://hatena.blog","width":"100%","blog_title":"present","type":"rich","author_url":"https://blog.hatena.ne.jp/griefworker/","provider_name":"Hatena Blog","blog_url":"https://tnakamura.hatenablog.com/","author_name":"griefworker","url":"https://tnakamura.hatenablog.com/entry/2017/02/15/gradient-descent","description":"C# \u3067\u30bc\u30ed\u304b\u3089 Deep Learning \u3092\u5b9f\u88c5\u3059\u308b\u6311\u6226\u306f\u307e\u3060 4 \u7ae0\u3002 \u6a5f\u68b0\u5b66\u7fd2\u3067\u4f7f\u3046\u52fe\u914d\u964d\u4e0b\u6cd5\u3092\u5b9f\u88c5\u3057\u3066\u307f\u305f\u3002 \u52fe\u914d\u3092\u8a08\u7b97\u3059\u308b\u30e1\u30bd\u30c3\u30c9\u306f\u524d\u56de\u8a18\u4e8b\u3092\u6d41\u7528\u3057\u3066\u3044\u308b\u3002 using MathNet.Numerics.LinearAlgebra; using System; using System.Linq; namespace GradientDescentSample { class Program { static void Main(string[] args) { // f(x0, x1) = x0^2 + x1^2 Func<Vector<double>, double> func\u2026","title":"\u52fe\u914d\u964d\u4e0b\u6cd5\u3092\u5b9f\u88c5\u3057\u3066\u307f\u305f","published":"2017-02-15 14:57:18"}