{"published":"2019-05-21 11:21:48","version":"1.0","height":"190","description":"Table of contents Table of contents Introduction Author GitHub Linear model with 1 dimensional input Input data: Age Target data: Height Data generation Linear model definition Gradient method Learning Result Point to notice Plane model with 2 dimensional input Data generation Plane model D-dimensio\u2026","author_name":"sy4310","width":"100%","blog_title":"EurekaMoments","image_url":"https://images-fe.ssl-images-amazon.com/images/I/518fpg%2B2MKL._SL160_.jpg","author_url":"https://blog.hatena.ne.jp/sy4310/","categories":["Python","Machine Learning"],"provider_name":"Hatena Blog","provider_url":"https://hatena.blog","type":"rich","url":"https://www.eureka-moments-blog.com/entry/2019/05/21/112148","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.eureka-moments-blog.com%2Fentry%2F2019%2F05%2F21%2F112148\" title=\"Fundamentals of Regression by Machine Learning - EurekaMoments\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","title":"Fundamentals of Regression by Machine Learning","blog_url":"https://www.eureka-moments-blog.com/"}