{"id":5855,"date":"2021-12-20T18:57:25","date_gmt":"2021-12-20T10:57:25","guid":{"rendered":"http:\/\/43.154.99.198\/?p=5855"},"modified":"2022-11-01T20:42:31","modified_gmt":"2022-11-01T12:42:31","slug":"fundamentals-of-machine-learning-and-linear-models","status":"publish","type":"post","link":"https:\/\/www.wangonc.com\/index.php\/2021\/12\/20\/fundamentals-of-machine-learning-and-linear-models\/","title":{"rendered":"\u673a\u5668\u5b66\u4e60\u57fa\u7840\u4e0e\u7ebf\u6027\u6a21\u578b\uff08\u4e00\uff09"},"content":{"rendered":"
This entry is \u7b2c 1 \u90e8\u5206\uff0c\u5171 3 in the series \u673a\u5668\u5b66\u4e60<\/a><\/div><\/div><\/div>

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\n

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h_\\theta(x) = \\theta_0+\\theta_1x<\/code><\/pre>\n
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J(\\theta_0,\\theta_1) = \\frac{1}{2m}\\sum_{i=1}^{m} ( h_{\\theta}(x^ {(i)})-y^ {( i )} )^2<\/code><\/pre>\n
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\n
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\\theta j:=\\theta _j-\\alpha \\frac {\\partial}{\\partial \\theta_j} J(\\theta_0,\\theta_1)<\/code><\/pre>\n
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This entry is \u7b2c 1 \u90e8\u5206\uff0c\u5171 3 in the series \u673a\u5668\u5b66\u4e60<\/a><\/div><\/div><\/div>
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