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\u4e8c\u3001\u6df1\u5ea6\u5b66\u4e60\uff1a\u5927\u7e41\u8363\u540e\u906d\u9047\u53d1\u5c55\u74f6\u9888<\/section>\n

\u6bcb\u5eb8\u8bb3\u8a00\uff0c\u4ee5\u6df1\u5ea6\u5b66\u4e60\u4e3a\u4ee3\u8868\u7684 AI \u7814\u7a76\u8fd9\u51e0\u5e74\u53d6\u5f97\u4e86\u8bf8\u591a\u4ee4\u4eba\u8d5e\u53f9\u7684\u8fdb\u6b65\uff0c\u6bd4\u5982\u5728\u590d\u6742\u7f51\u7edc\u7684\u8bad\u7ec3\u65b9\u9762\uff0c\u4ea7\u751f\u4e86\u4e24\u4e2a\u7279\u522b\u6210\u529f\u7684\u7f51\u7edc\u7ed3\u6784\uff0cCNN \u548c transformer\u3002\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\uff0cAI \u7814\u7a76\u8005\u5728\u8bed\u97f3\u3001\u8bed\u4e49\u3001\u89c6\u89c9\u7b49\u5404\u4e2a\u9886\u57df\u90fd\u5b9e\u73b0\u4e86\u5feb\u901f\u7684\u53d1\u5c55\uff0c\u89e3\u51b3\u4e86\u8bf8\u591a\u73b0\u5b9e\u96be\u9898\uff0c\u5b9e\u73b0\u4e86\u5de8\u5927\u7684\u793e\u4f1a\u4ef7\u503c\u3002<\/p>\n

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\u9996\u5148\u662f\u968f\u673a\u68af\u5ea6\u4e0b\u964d\uff08SGD\uff09\uff0c\u6781\u5927\u63a8\u52a8\u4e86\u6df1\u5ea6\u5b66\u4e60\u7684\u53d1\u5c55\u3002\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u5176\u5b9e\u662f\u4e00\u4e2a\u5f88\u7b80\u5355\u7684\u65b9\u6cd5\uff0c\u5177\u6709\u8f83\u5927\u5c40\u9650\u6027\uff0c\u5728\u4f18\u5316\u91cc\u9762\u5c5e\u4e8e\u6536\u655b\u8f83\u6162\u7684\u65b9\u6cd5\uff0c\u4f46\u5b83\u504f\u504f\u5728\u6df1\u5ea6\u7f51\u7edc\u4e2d\u8868\u73b0\u5f88\u597d\uff0c\u800c\u4e14\u8fd8\u662f\u51fa\u5947\u7684\u597d\u3002\u4e3a\u4ec0\u4e48\u4f1a\u8fd9\u4e48\u597d\uff1f\u8fc4\u4eca\u7814\u7a76\u8005\u90fd\u6ca1\u6709\u5b8c\u7f8e\u7684\u7b54\u6848\u3002\u7c7b\u4f3c\u8fd9\u6837\u96be\u4ee5\u7406\u89e3\u7684\u597d\u8fd0\u6c14\u8fd8\u5305\u62ec\u6b8b\u5dee\u7f51\u7edc\u3001\u77e5\u8bc6\u84b8\u998f\u3001Batch Normalization\u3001Warmup\u3001Label Smoothing\u3001Gradient Clip\u3001Layer Scaling\u2026\u5c24\u5176\u662f\u6709\u4e9b\u8fd8\u5177\u6709\u8d85\u5f3a\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u80fd\u7528\u5728\u591a\u4e2a\u573a\u666f\u4e2d\u3002<\/p>\n

\u518d\u8005\uff0c\u5728\u673a\u5668\u5b66\u4e60\u91cc\uff0c\u7814\u7a76\u8005\u4e00\u76f4\u5728\u8b66\u60d5\u8fc7\u62df\u5408\uff08overfitting\uff09\u7684\u95ee\u9898\u3002\u5f53\u53c2\u6570\u7279\u522b\u591a\u65f6\uff0c\u4e00\u6761\u66f2\u7ebf\u80fd\u591f\u628a\u6240\u6709\u7684\u70b9\u90fd\u62df\u5408\u5f97\u7279\u522b\u597d\uff0c\u5b83\u5927\u6982\u7387\u5b58\u5728\u95ee\u9898\uff0c\u4f46\u5728\u6df1\u5ea6\u5b66\u4e60\u91cc\u9762\u8fd9\u4f3c\u4e4e\u4e0d\u518d\u6210\u4e3a\u4e00\u4e2a\u95ee\u9898\u2026\u867d\u7136\u6709\u5f88\u591a\u7814\u7a76\u8005\u5bf9\u6b64\u8fdb\u884c\u4e86\u63a2\u8ba8\uff0c\u4f46\u76ee\u524d\u8fd8\u6709\u6ca1\u6709\u660e\u786e\u7b54\u6848\u3002\u66f4\u52a0\u4ee4\u4eba\u60ca\u8bb6\u7684\u662f\uff0c\u6211\u4eec\u5373\u4f7f\u7ed9\u6570\u636e\u4e00\u4e2a\u968f\u673a\u7684\u6807\u7b7e\uff0c\u5b83\u4e5f\u53ef\u4ee5\u5b8c\u7f8e\u62df\u5408\uff08\u8bf7\u89c1\u4e0b\u56fe\u7ea2\u8272\u66f2\u7ebf\uff09\uff0c\u6700\u540e\u5f97\u51fa\u62df\u5408\u8bef\u5dee\u4e3a 0\u3002\u5982\u679c\u6309\u7167\u6807\u51c6\u7406\u8bba\u6765\u8bf4\uff0c\u8fd9\u610f\u5473\u7740\u8fd9\u4e2a\u6a21\u578b\u6ca1\u6709\u4efb\u4f55\u504f\u5dee\uff08bias\uff09\uff0c\u80fd\u5e2e\u6211\u4eec\u89e3\u91ca\u4efb\u4f55\u7ed3\u679c\u3002\u8bf7\u60f3\u60f3\u770b\uff0c\u4efb\u4f55\u4e1c\u897f\u90fd\u80fd\u89e3\u91ca\u7684\u6a21\u578b\uff0c\u771f\u7684\u53ef\u9760\u5417\uff0c\u5305\u6cbb\u767e\u75c5\u7684\u826f\u836f\u53ef\u4fe1\u5417\uff1f<\/p>\n

\"\u56fe\u7247\"\uff08Understanding deep learning requires rethinking generalization. ICLR, 2017.\uff09<\/em><\/p>\n

\u8bf4\u5230\u8fd9\u91cc\uff0c\u8ba9\u6211\u4eec\u6574\u4f53\u56de\u987e\u4e0b\u673a\u5668\u5b66\u4e60\u7684\u53d1\u5c55\u5386\u7a0b\uff0c\u624d\u80fd\u66f4\u597d\u7406\u89e3\u5f53\u4e0b\u7684\u6df1\u5ea6\u5b66\u4e60\u3002<\/p>\n

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\u4f46\u662f\u6df1\u5ea6\u5b66\u4e60\u65f6\u4ee3\uff0c\u6709\u4eba\u505a\u51fa\u4e86\u975e\u5e38\u597d\u7684\u6846\u67b6\uff0c\u4fbf\u5229\u4e86\u6240\u6709\u7684\u7814\u7a76\u8005\uff0c\u964d\u4f4e\u4e86\u95e8\u69db\uff0c\u8fd9\u771f\u662f\u975e\u5e38\u4e86\u4e0d\u8d77\u7684\u4e8b\u60c5\uff0c\u4fc3\u8fdb\u4e86\u884c\u4e1a\u7684\u5feb\u901f\u53d1\u5c55\u3002\u4eca\u5929\u53bb\u505a\u6df1\u5ea6\u5b66\u4e60\uff0c\u6709\u4e2a\u597d\u60f3\u6cd5\u5c31\u53ef\u4ee5\u5e72\uff0c\u53ea\u8981\u5199\u4e0a\u51e0\u5341\u884c\u3001\u751a\u81f3\u5341\u51e0\u884c\u4ee3\u7801\u5c31\u53ef\u4ee5\u8dd1\u8d77\u6765\u3002\u6210\u5343\u4e0a\u4e07\u4eba\u5728\u5b9e\u9a8c\u5404\u79cd\u5404\u6837\u7684\u65b0\u9879\u76ee\uff0c\u9a8c\u8bc1\u5404\u79cd\u5404\u6837\u65b0\u60f3\u6cd5\uff0c\u7ecf\u5e38\u4f1a\u5192\u51fa\u6765\u975e\u5e38\u8ba9\u4eba\u60ca\u559c\u7684\u7ed3\u679c\u3002<\/p>\n

\u4f46\u6211\u4eec\u53ef\u80fd\u9700\u8981\u610f\u8bc6\u5230\uff0c\u65f6\u81f3\u4eca\u65e5\uff0c\u6df1\u5ea6\u5b66\u4e60\u5df2\u9047\u5230\u4e86\u5f88\u5927\u7684\u74f6\u9888\u3002\u90a3\u4e9b\u66fe\u7ecf\u5e2e\u52a9\u6df1\u5ea6\u5b66\u4e60\u6210\u529f\u7684\u597d\u8fd0\u6c14\uff0c\u90a3\u4e9b\u65e0\u6cd5\u7406\u89e3\u7684\u9ed1\u76d2\u6548\u5e94\uff0c\u4eca\u5929\u5df2\u6210\u4e3a\u5b83\u8fdb\u4e00\u6b65\u53d1\u5c55\u7684\u684e\u688f\u3002<\/p>\n

\u4e09\u3001\u4e0b\u4e00\u4ee3 AI \u7684\u4e09\u4e2a\u53ef\u80fd\u65b9\u5411<\/section>\n

AI \u7684\u672a\u6765\u7a76\u7adf\u5728\u54ea\u91cc\uff1f\u4e0b\u4e00\u4ee3 AI \u5c06\u662f\u4ec0\u4e48\uff1f\u76ee\u524d\u5f88\u96be\u7ed9\u51fa\u660e\u786e\u7b54\u6848\uff0c\u4f46\u6211\u8ba4\u4e3a\uff0c\u81f3\u5c11\u6709\u4e09\u4e2a\u65b9\u5411\u503c\u5f97\u91cd\u70b9\u63a2\u7d22\u548c\u7a81\u7834\u3002<\/p>\n

\u7b2c\u4e00\u4e2a\u65b9\u5411\u662f\u5bfb\u6c42\u5bf9\u6df1\u5ea6\u5b66\u4e60\u7684\u6839\u672c\u7406\u89e3<\/strong>\uff0c\u7834\u9664\u76ee\u524d\u7684\u9ed1\u76d2\u72b6\u6001\uff0c\u53ea\u6709\u8fd9\u6837 AI \u624d\u6709\u53ef\u80fd\u6210\u4e3a\u4e00\u95e8\u79d1\u5b66\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5e94\u8be5\u5305\u62ec\u5bf9\u4ee5\u4e0b\u5173\u952e\u95ee\u9898\u7684\u7a81\u7834\uff1a<\/p>\n

    \n
  • \u5bf9\u57fa\u4e8e DNN \u51fd\u6570\u7a7a\u95f4\u7684\u66f4\u5168\u9762\u523b\u753b\uff1b<\/li>\n
  • \u5bf9 SGD\uff08\u6216\u66f4\u5e7f\u4e49\u7684\u4e00\u9636\u4f18\u5316\u7b97\u6cd5\uff09\u7684\u7406\u89e3\uff1b<\/li>\n
  • \u91cd\u65b0\u8003\u8651\u6cdb\u5316\u7406\u8bba\u7684\u57fa\u7840\u3002<\/li>\n<\/ul>\n

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    \u56db\u3001AI \u7684\u673a\u4f1a\uff1aAI for Science<\/section>\n

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