{"version":"1.0","height":"190","blog_title":"present","categories":["C#","\u6a5f\u68b0\u5b66\u7fd2"],"type":"rich","url":"https://tnakamura.hatenablog.com/entry/2017/02/08/numerical-differentiation","published":"2017-02-08 14:23:46","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Ftnakamura.hatenablog.com%2Fentry%2F2017%2F02%2F08%2Fnumerical-differentiation\" title=\"\u6570\u5024\u5fae\u5206\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>","blog_url":"https://tnakamura.hatenablog.com/","author_url":"https://blog.hatena.ne.jp/griefworker/","title":"\u6570\u5024\u5fae\u5206\u3092\u5b9f\u88c5\u3057\u3066\u307f\u305f","width":"100%","provider_url":"https://hatena.blog","author_name":"griefworker","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/g/griefworker/20170208/20170208142158.png","description":"C# \u3067\u30bc\u30ed\u304b\u3089 Deep Learning \u3092\u5b9f\u88c5\u3059\u308b\u6311\u6226\u306e\u7d9a\u304d\u3002 \u4eca\u56de\u306f\u7b2c 4 \u7ae0\u306e\u6570\u5024\u5fae\u5206\u3092\u5b9f\u88c5\u3057\u3066\u307f\u305f\u3002 using System; namespace NumericalDifferentiationSample { class Program { static void Main(string[] args) { // \u5fae\u5206 Func<double, double> function1 = x => 0.01 * Math.Pow(x, 2) + 0.1 * x; Console.WriteLine(\"y = 0.01x^2 + 0.1x\"); Console.WriteLine(\u2026","provider_name":"Hatena Blog"}