{"id":65873,"date":"2026-01-25T20:04:55","date_gmt":"2026-01-25T12:04:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/65873.html"},"modified":"2026-01-25T20:04:55","modified_gmt":"2026-01-25T12:04:55","slug":"%e5%a4%a7%e5%8e%82ai%e7%ae%97%e6%b3%95%e9%9d%a2%e8%af%95%e9%a2%98%e6%b1%87%e6%80%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/65873.html","title":{"rendered":"\u5927\u5382AI\u7b97\u6cd5\u9762\u8bd5\u9898\u6c47\u603b"},"content":{"rendered":"

\u5927\u5382AI\u7b97\u6cd5\u9762\u8bd5\u9898\u6c47\u603b<\/h2>\n
  • \n

    Q：\u5982\u4f55\u53cd\u8f6c\u4e00\u4e2a\u5355\u5411\u94fe\u8868？ A：\u4f7f\u7528\u53cc\u6307\u9488\u8fed\u4ee3\u6cd5\u3002\u521d\u59cb\u5316 pre = None，cur = head；\u904d\u5386\u94fe\u8868，\u6bcf\u6b21\u4fdd\u5b58 cur.next，\u5c06 cur.next \u6307\u5411 pre，\u7136\u540e\u66f4\u65b0 pre = cur，cur = nxt；\u6700\u7ec8\u8fd4\u56de pre\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5feb\u6392\u7684\u65f6\u95f4\u590d\u6742\u5ea6\u662f\u591a\u5c11？\u6700\u574f\u60c5\u51b5\u5982\u4f55\u89e6\u53d1？ A：\u5e73\u5747\u65f6\u95f4\u590d\u6742\u5ea6\u4e3a O(n log n)，\u6700\u574f\u4e3a O(n\u00b2)\u3002\u5f53\u6bcf\u6b21\u9009\u7684 pivot \u662f\u6700\u5927\u6216\u6700\u5c0f\u503c（\u5982\u5df2\u6392\u5e8f\u6570\u7ec4\u4e14\u9009\u9996\u5143\u7d20\u4e3a pivot）\u65f6\u89e6\u53d1\u6700\u574f\u60c5\u51b5\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u5224\u65ad\u4e00\u68f5\u4e8c\u53c9\u6811\u662f\u5426\u662f\u5e73\u8861\u4e8c\u53c9\u6811？ A：\u9012\u5f52\u8ba1\u7b97\u5de6\u53f3\u5b50\u6811\u9ad8\u5ea6，\u82e5\u4efb\u610f\u5b50\u6811\u9ad8\u5ea6\u5dee >1，\u5219\u4e0d\u5e73\u8861\u3002\u540c\u65f6\u5728\u9012\u5f52\u4e2d\u8fd4\u56de\u9ad8\u5ea6，\u907f\u514d\u91cd\u590d\u8ba1\u7b97\u3002<\/p>\n<\/li>\n

  • \n

    Q：LeetCode \u6700\u957f\u516c\u5171\u5b50\u5e8f\u5217（LCS）\u600e\u4e48\u89e3？ A：\u52a8\u6001\u89c4\u5212\u3002\u8bbe dp[i][j] \u8868\u793a s1[:i] \u4e0e s2[:j] \u7684 LCS \u957f\u5ea6\u3002\u72b6\u6001\u8f6c\u79fb：\u82e5 s1[i-1]==s2[j-1]，\u5219 dp[i][j]=dp[i-1][j-1]+1；\u5426\u5219 dp[i][j]=max(dp[i-1][j], dp[i][j-1])\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u7f16\u8f91\u8ddd\u79bb\u7684 DP \u72b6\u6001\u8f6c\u79fb\u65b9\u7a0b\u662f\u4ec0\u4e48？ A：dp[i][j] = min(dp[i-1][j] + 1, dp[i][j-1] + 1, dp[i-1][j-1] + (s1[i-1]!=s2[j-1]))，\u5206\u522b\u5bf9\u5e94\u5220\u9664\u3001\u63d2\u5165\u3001\u66ff\u6362\u64cd\u4f5c\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u7528 rand5() \u5b9e\u73b0 rand7()？ A：\u6784\u9020 0~24 \u7684\u5747\u5300\u5206\u5e03：num = (rand5()-1)*5 + (rand5()-1)；\u82e5 num < 21，\u5219\u8fd4\u56de num % 7 + 1；\u5426\u5219\u91cd\u8bd5\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u629b\u786c\u5e01\u76f4\u5230\u8fde\u7eed\u4e09\u6b21\u6b63\u9762，\u671f\u671b\u6b21\u6570\u662f\u591a\u5c11？ A：14 \u6b21\u3002\u901a\u8fc7\u72b6\u6001\u8f6c\u79fb\u65b9\u7a0b\u6c42\u89e3：\u8bbe E\u2080 \u4e3a\u521d\u59cb\u72b6\u6001，E\u2081 \u4e3a\u5df2\u67091\u4e2aH，E\u2082 \u4e3a\u5df2\u67092\u4e2aH，\u5217\u65b9\u7a0b\u89e3\u5f97 E\u2080=14\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5355\u4f4d\u5706\u5185\u968f\u673a\u53d6\u4e00\u70b9，\u5230\u5706\u5fc3\u8ddd\u79bb\u7684\u671f\u671b\u662f\u591a\u5c11？ A：2\/3\u3002\u63a8\u5bfc：\u534a\u5f84 r \u7684\u6982\u7387\u5bc6\u5ea6\u4e3a f\u00ae=2r（\u56e0\u9762\u79ef\u5143\u4e3a 2\u03c0r dr），\u6545 E[r] = \u222b\u2080\u00b9 r\u00b72r dr = 2\/3\u3002<\/p>\n<\/li>\n

  • \n

    Q：Python \u4e2d\u6df1\u62f7\u8d1d\u548c\u6d45\u62f7\u8d1d\u7684\u533a\u522b？ A：\u6d45\u62f7\u8d1d\u53ea\u590d\u5236\u9876\u5c42\u5bf9\u8c61，\u5d4c\u5957\u5bf9\u8c61\u4ecd\u5171\u4eab\u5f15\u7528；\u6df1\u62f7\u8d1d\u9012\u5f52\u590d\u5236\u6240\u6709\u5c42\u7ea7，\u5b8c\u5168\u72ec\u7acb\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u591a\u8fdb\u7a0b\u548c\u591a\u7ebf\u7a0b\u5728 Python \u4e2d\u5982\u4f55\u9009\u62e9？ A：CPU \u5bc6\u96c6\u578b\u4efb\u52a1\u7528\u591a\u8fdb\u7a0b（\u7ed5\u8fc7 GIL）；IO \u5bc6\u96c6\u578b\u4efb\u52a1\u7528\u591a\u7ebf\u7a0b（\u5982\u7f51\u7edc\u8bf7\u6c42\u3001\u6587\u4ef6\u8bfb\u5199）\u3002<\/p>\n<\/li>\n

  • \n

    Q：TCP \u548c UDP \u7684\u4e3b\u8981\u533a\u522b\u662f\u4ec0\u4e48？ A：TCP \u9762\u5411\u8fde\u63a5\u3001\u53ef\u9760\u3001\u6709\u5e8f\u3001\u6162；UDP \u65e0\u8fde\u63a5\u3001\u4e0d\u53ef\u9760\u3001\u53ef\u80fd\u4e71\u5e8f\u3001\u5feb\u3002\u9002\u7528\u4e8e\u89c6\u9891\u6d41\u3001DNS \u7b49\u573a\u666f\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4ec0\u4e48\u662f\u88c5\u9970\u5668？\u4e3e\u4e00\u4e2a\u65e5\u5fd7\u88c5\u9970\u5668\u7684\u4f8b\u5b50\u3002 A：\u88c5\u9970\u5668\u662f\u9ad8\u9636\u51fd\u6570，\u7528\u4e8e\u589e\u5f3a\u51fd\u6570\u529f\u80fd\u3002\u4f8b\u5982：<\/p>\n

    def<\/span> log<\/span>(<\/span>func)<\/span>:<\/span>
    \n def<\/span> wrapper<\/span>(<\/span>*<\/span>args,<\/span> **<\/span>kwargs)<\/span>:<\/span>
    \n print<\/span>(<\/span>f"Calling <\/span>{<\/span>func.<\/span>__name__}<\/span><\/span>"<\/span><\/span>)<\/span>
    \n return<\/span> func(<\/span>*<\/span>args,<\/span> **<\/span>kwargs)<\/span>
    \n return<\/span> wrapper\n <\/li>\n

  • \n

    Q：Self-Attention \u7684\u8ba1\u7b97\u516c\u5f0f\u662f\u4ec0\u4e48？ A：Attention(Q,K,V) = softmax(QK\u1d40 \/ \u221ad_k) V，\u5176\u4e2d Q\u3001K\u3001V \u7531\u8f93\u5165\u7ebf\u6027\u53d8\u6362\u5f97\u5230\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4e3a\u4ec0\u4e48 Self-Attention \u8981\u9664\u4ee5 \u221ad_k？ A：\u9632\u6b62\u70b9\u79ef\u8fc7\u5927\u5bfc\u81f4 Softmax \u68af\u5ea6\u6d88\u5931\u3002\u5f53 d_k \u5927\u65f6，q\u00b7k \u65b9\u5dee\u589e\u5927，\u9664\u4ee5 \u221ad_k \u53ef\u4f7f\u65b9\u5dee\u7a33\u5b9a\u4e3a 1\u3002<\/p>\n<\/li>\n

  • \n

    Q：Multi-Head Attention \u7684\u4f5c\u7528\u662f\u4ec0\u4e48？ A：\u8ba9\u6a21\u578b\u5728\u4e0d\u540c\u5b50\u7a7a\u95f4\u4e2d\u5b66\u4e60\u4e0d\u540c\u7684\u7279\u5f81\u4ea4\u4e92，\u7c7b\u4f3c CNN \u7684\u591a\u901a\u9053，\u63d0\u5347\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n<\/li>\n

  • \n

    Q：Transformer \u4e3a\u4ec0\u4e48\u7528 LayerNorm \u800c\u4e0d\u662f BatchNorm？ A：\u56e0\u4e3a Transformer \u5904\u7406\u53d8\u957f\u5e8f\u5217，batch \u5185\u957f\u5ea6\u4e0d\u4e00，BatchNorm \u5bf9\u5c0f batch \u654f\u611f；LayerNorm \u5bf9\u5355\u6837\u672c\u5f52\u4e00\u5316，\u66f4\u7a33\u5b9a\u3002<\/p>\n<\/li>\n

  • \n

    Q：BERT \u7684\u8f93\u5165\u5305\u542b\u54ea\u4e09\u90e8\u5206？ A：Token Embedding（\u8bcd\u5411\u91cf）\u3001Segment Embedding（\u53e5\u5b50A\/B\u6807\u8bc6）\u3001Position Embedding（\u4f4d\u7f6e\u7f16\u7801）\u3002<\/p>\n<\/li>\n

  • \n

    Q：BERT \u4e3a\u4ec0\u4e48\u4e0d\u9002\u5408\u76f4\u63a5\u7528\u4e8e\u53e5\u5411\u91cf\u76f8\u4f3c\u5ea6\u8ba1\u7b97？ A：\u56e0\u5176 embedding \u5b58\u5728\u5404\u5411\u5f02\u6027（anisotropy），\u5411\u91cf\u5206\u5e03\u4e0d\u5747，\u5bfc\u81f4\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5931\u771f\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u6539\u8fdb BERT \u7684\u53e5\u5411\u91cf\u8868\u793a？ A：\u4f7f\u7528 Sentence-BERT（\u5fae\u8c03\u65f6\u76f4\u63a5\u4f18\u5316\u4f59\u5f26\u76f8\u4f3c\u5ea6），\u6216\u7528 BERT-Flow \u5c06 embedding \u6620\u5c04\u5230\u5404\u5411\u540c\u6027\u7a7a\u95f4\u3002<\/p>\n<\/li>\n

  • \n

    Q：ResNet \u7684\u6838\u5fc3\u601d\u60f3\u662f\u4ec0\u4e48？ A：\u5f15\u5165\u6b8b\u5dee\u8fde\u63a5（skip connection），\u8ba9\u7f51\u7edc\u5b66\u4e60\u6b8b\u5dee\u6620\u5c04 F(x) = H(x) – x，\u7f13\u89e3\u68af\u5ea6\u6d88\u5931\u548c\u7f51\u7edc\u9000\u5316\u3002<\/p>\n<\/li>\n

  • \n

    Q：Dropout \u5728\u8bad\u7ec3\u548c\u6d4b\u8bd5\u65f6\u5982\u4f55\u5904\u7406？ A：\u8bad\u7ec3\u65f6\u968f\u673a\u5c06\u90e8\u5206\u795e\u7ecf\u5143\u8f93\u51fa\u7f6e0，\u5e76\u5c06\u5269\u4f59\u8f93\u51fa\u9664\u4ee5 (1-p)；\u6d4b\u8bd5\u65f6\u5173\u95ed Dropout，\u76f4\u63a5\u524d\u5411\u4f20\u64ad\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4e3a\u4ec0\u4e48\u6d4b\u8bd5\u65f6\u4e0d\u5173\u95ed Dropout \u800c\u662f\u4fdd\u6301\u671f\u671b\u4e00\u81f4？ A：\u4e3a\u4fdd\u8bc1\u8bad\u7ec3\u548c\u6d4b\u8bd5\u65f6\u6fc0\u6d3b\u503c\u7684\u671f\u671b\u76f8\u540c，\u907f\u514d\u5206\u5e03\u504f\u79fb\u3002\u7f29\u653e\u64cd\u4f5c\u4f7f E[output_train] = E[output_test]\u3002<\/p>\n<\/li>\n

  • \n

    Q：L1 \u6b63\u5219\u4e3a\u4f55\u80fd\u4ea7\u751f\u7a00\u758f\u89e3？ A：L1 \u7684\u68af\u5ea6\u4e3a\u5e38\u6570 \u03bb\u00b7sign(w)，\u5373\u4f7f w \u63a5\u8fd10\u4ecd\u6709\u6052\u5b9a\u68af\u5ea6\u5c06\u5176\u63a8\u54110；\u800c L2 \u68af\u5ea6\u968f w \u51cf\u5c0f\u800c\u51cf\u5c0f，\u8d8b\u8fd1\u4f46\u4e0d\u7b49\u4e8e0\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4ea4\u53c9\u71b5\u635f\u5931 vs \u5747\u65b9\u8bef\u5dee（MSE）\u5728\u5206\u7c7b\u4efb\u52a1\u4e2d\u7684\u533a\u522b？ A：\u4ea4\u53c9\u71b5\u57fa\u4e8e\u6982\u7387\u5206\u5e03，\u68af\u5ea6\u4e3a (y – p)，\u4fe1\u606f\u91cf\u5927；MSE \u68af\u5ea6\u4e3a (y – p)\u00b7p\u00b7(1-p)，\u5728 p \u63a5\u8fd10\/1\u65f6\u68af\u5ea6\u6d88\u5931\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u591a\u6807\u7b7e\u5206\u7c7b\u7684\u8f93\u51fa\u5c42\u548c\u635f\u5931\u51fd\u6570\u662f\u4ec0\u4e48？ A：\u8f93\u51fa\u5c42\u7528 Sigmoid（\u6bcf\u4e2a\u6807\u7b7e\u72ec\u7acb），\u635f\u5931\u51fd\u6570\u7528 Binary Cross-Entropy（BCE）\u3002<\/p>\n<\/li>\n

  • \n

    Q：Focal Loss \u7684\u516c\u5f0f\u548c\u4f5c\u7528\u662f\u4ec0\u4e48？ A：FL(p_t) = -\u03b1_t (1 – p_t)^\u03b3 log(p_t)\u3002\u901a\u8fc7 (1-p_t)^\u03b3 \u964d\u4f4e\u6613\u5206\u6837\u672c\u6743\u91cd，\u805a\u7126\u96be\u6837\u672c，\u89e3\u51b3\u7c7b\u522b\u4e0d\u5e73\u8861\u3002<\/p>\n<\/li>\n

  • \n

    Q：XGBoost \u7684\u76ee\u6807\u51fd\u6570\u5305\u542b\u54ea\u4e9b\u9879？ A：\u5305\u62ec\u635f\u5931\u51fd\u6570（\u5982 log loss）\u7684\u4e00\u9636\u548c\u4e8c\u9636\u68af\u5ea6\u5c55\u5f00\u9879，\u4ee5\u53ca\u6b63\u5219\u9879（\u03b3T + \u00bd\u03bb||w||\u00b2），\u5176\u4e2d T \u4e3a\u53f6\u5b50\u6570，w \u4e3a\u53f6\u5b50\u6743\u91cd\u3002<\/p>\n<\/li>\n

  • \n

    Q：XGBoost \u4e3a\u4f55\u4f7f\u7528\u4e8c\u9636\u6cf0\u52d2\u5c55\u5f00？ A：\u53ef\u7edf\u4e00\u5904\u7406\u4efb\u610f\u4e8c\u9636\u53ef\u5bfc\u635f\u5931\u51fd\u6570，\u5e76\u5229\u7528\u66f2\u7387\u4fe1\u606f\u52a0\u901f\u6536\u655b\u3002<\/p>\n<\/li>\n

  • \n

    Q：LightGBM \u7684 Leaf-wise \u5206\u88c2\u7b56\u7565\u662f\u4ec0\u4e48？ A：\u6bcf\u6b21\u9009\u62e9\u589e\u76ca\u6700\u5927\u7684\u53f6\u5b50\u8282\u70b9\u5206\u88c2，\u800c\u975e\u6309\u5c42\u5206\u88c2（Level-wise），\u66f4\u9ad8\u6548\u4f46\u53ef\u80fd\u8fc7\u62df\u5408，\u9700\u9650\u5236 max_depth\u3002<\/p>\n<\/li>\n

  • \n

    Q：LightGBM \u5982\u4f55\u5904\u7406\u7c7b\u522b\u7279\u5f81？ A：\u539f\u751f\u652f\u6301\u3002\u5bf9\u7c7b\u522b\u7279\u5f81，\u6309\u76f4\u65b9\u56fe\u7edf\u8ba1\u68af\u5ea6，\u81ea\u52a8\u5bfb\u627e\u6700\u4f18\u5206\u5272（\u5982\u6309\u7c7b\u522b\u96c6\u5408\u5212\u5206）\u3002<\/p>\n<\/li>\n

  • \n

    Q：GBDT \u4e3a\u4ec0\u4e48\u4e0d\u80fd\u4f7f\u7528\u5e73\u65b9\u635f\u5931\u5904\u7406\u5206\u7c7b\u95ee\u9898？ A：\u53ef\u4ee5，\u4f46\u6548\u7387\u4f4e\u3002GBDT \u901a\u5e38\u7528\u6307\u6570\u635f\u5931（AdaBoost）\u6216\u5bf9\u6570\u635f\u5931（LogitBoost）\u66f4\u9002\u5408\u5206\u7c7b\u3002<\/p>\n<\/li>\n

  • \n

    Q：Wide & Deep \u6a21\u578b\u7684 Wide \u90e8\u5206\u4f5c\u7528\u662f\u4ec0\u4e48？ A：\u8bb0\u5fc6\u9ad8\u9891\u5171\u73b0\u7279\u5f81\u7ec4\u5408（\u5982\u201c\u7528\u6237A+\u5546\u54c1B\u201d），\u63d0\u5347\u51c6\u786e\u7387\u548c\u53ef\u89e3\u91ca\u6027\u3002<\/p>\n<\/li>\n

  • \n

    Q：DeepFM \u76f8\u6bd4 Wide & Deep \u7684\u6539\u8fdb\u662f\u4ec0\u4e48？ A：\u7528 FM \u66ff\u4ee3 Wide \u90e8\u5206，\u81ea\u52a8\u5b66\u4e60\u4e8c\u9636\u7279\u5f81\u4ea4\u53c9，\u65e0\u9700\u4eba\u5de5\u8bbe\u8ba1\u4ea4\u53c9\u7279\u5f81\u3002<\/p>\n<\/li>\n

  • \n

    Q：BatchNorm \u5728\u8bad\u7ec3\u548c\u63a8\u7406\u65f6\u6709\u4f55\u4e0d\u540c？ A：\u8bad\u7ec3\u65f6\u7528\u5f53\u524d batch \u7684\u5747\u503c\u548c\u65b9\u5dee；\u63a8\u7406\u65f6\u7528\u8bad\u7ec3\u9636\u6bb5\u7d2f\u79ef\u7684\u6ed1\u52a8\u5e73\u5747\u5747\u503c\u548c\u65b9\u5dee\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4e3a\u4ec0\u4e48 RNN \u96be\u4ee5\u5e76\u884c？ A：\u56e0\u4e3a\u6bcf\u4e00\u6b65\u7684\u9690\u85cf\u72b6\u6001\u4f9d\u8d56\u524d\u4e00\u6b65\u8f93\u51fa，\u5b58\u5728\u65f6\u95f4\u4e0a\u7684\u4e32\u884c\u4f9d\u8d56\u3002<\/p>\n<\/li>\n

  • \n

    Q：Transformer \u5982\u4f55\u5b9e\u73b0\u5e76\u884c？ A：\u629b\u5f03\u5faa\u73af\u7ed3\u6784，\u6240\u6709\u4f4d\u7f6e\u901a\u8fc7 Self-Attention \u540c\u65f6\u8ba1\u7b97，\u5b8c\u5168\u5e76\u884c\u3002<\/p>\n<\/li>\n

  • \n

    Q：Positional Encoding \u7684\u4f5c\u7528\u662f\u4ec0\u4e48？ A：\u4e3a\u8f93\u5165\u5e8f\u5217\u6ce8\u5165\u4f4d\u7f6e\u4fe1\u606f，\u5f25\u8865 Transformer \u65e0\u5e8f\u6027\u7684\u7f3a\u9677\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4e3a\u4ec0\u4e48 Positional Encoding \u7528 sin\/cos \u800c\u4e0d\u7528\u53ef\u5b66\u4e60\u5411\u91cf？ A：sin\/cos \u5177\u6709\u5468\u671f\u6027\u548c\u53ef\u6269\u5c55\u6027，\u80fd\u6cdb\u5316\u5230\u8bad\u7ec3\u65f6\u672a\u89c1\u7684\u5e8f\u5217\u957f\u5ea6\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u5904\u7406 BERT \u7684\u957f\u6587\u672c（>512 tokens）？ A：\u5e38\u7528\u65b9\u6cd5：head-only（\u53d6\u524d512）\u3001tail-only（\u53d6\u540e512）\u3001head+tail（\u5982128+384），\u6216\u5206\u6bb5\u540e\u7528 Pooling\/Attention \u878d\u5408\u3002<\/p>\n<\/li>\n

  • \n

    Q：ONNX \u7684\u4f5c\u7528\u662f\u4ec0\u4e48？ A：\u63d0\u4f9b\u6a21\u578b\u4e2d\u95f4\u8868\u793a\u683c\u5f0f，\u5b9e\u73b0\u8de8\u6846\u67b6（PyTorch\/TensorFlow）\u90e8\u7f72，\u914d\u5408 ONNX Runtime \u52a0\u901f\u63a8\u7406\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u6a21\u578b\u91cf\u5316\u7684\u4f5c\u7528\u548c\u98ce\u9669？ A：\u4f5c\u7528：\u51cf\u5c11\u6a21\u578b\u4f53\u79ef\u3001\u52a0\u901f\u63a8\u7406（INT8 \u6bd4 FP32 \u5feb2~4\u500d）；\u98ce\u9669：\u7cbe\u5ea6\u4e0b\u964d，\u9700\u6821\u51c6（calibration）\u907f\u514d\u5d29\u584c\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u5224\u65ad\u4e24\u4e2a\u77e9\u5f62\u662f\u5426\u76f8\u4ea4？ A：\u82e5\u4e24\u77e9\u5f62\u4e2d\u5fc3\u8ddd\u79bb\u5728 x\/y \u65b9\u5411\u5747\u5c0f\u4e8e\u534a\u5bbd\u4e4b\u548c，\u5219\u76f8\u4ea4\u3002\u6216\u7528\u5206\u79bb\u8f74\u5b9a\u7406（SAT）\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u5408\u5e76\u91cd\u53e0\u533a\u95f4？ A：\u5148\u6309\u5de6\u7aef\u70b9\u6392\u5e8f，\u7136\u540e\u904d\u5386：\u82e5\u5f53\u524d\u533a\u95f4\u4e0e\u7ed3\u679c\u672b\u5c3e\u91cd\u53e0，\u5219\u5408\u5e76；\u5426\u5219\u52a0\u5165\u65b0\u533a\u95f4\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u6700\u957f\u4e0d\u91cd\u590d\u5b50\u4e32\u7684\u89e3\u6cd5？ A：\u6ed1\u52a8\u7a97\u53e3 + \u54c8\u5e0c\u8868\u8bb0\u5f55\u5b57\u7b26\u6700\u8fd1\u4f4d\u7f6e\u3002\u53f3\u6307\u9488\u6269\u5c55，\u5de6\u6307\u9488\u8df3\u81f3\u91cd\u590d\u5b57\u7b26\u4e0b\u4e00\u4f4d\u3002<\/p>\n<\/li>\n

  • \n

    Q：Top-K \u95ee\u9898\u4e3a\u4f55\u7528\u5806\u800c\u4e0d\u662f\u5168\u6392\u5e8f？ A：\u5806\u65f6\u95f4\u590d\u6742\u5ea6 O(n log k)，\u7a7a\u95f4 O(k)；\u5168\u6392\u5e8f O(n log n)，\u5f53 k << n \u65f6\u5806\u66f4\u4f18\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u5b9e\u73b0 LRU \u7f13\u5b58？ A：\u7528\u54c8\u5e0c\u8868 + \u53cc\u5411\u94fe\u8868\u3002\u54c8\u5e0c\u8868 O(1) \u67e5\u627e，\u94fe\u8868\u7ef4\u62a4\u8bbf\u95ee\u987a\u5e8f，\u5934\u63d2\u65b0\u5143\u7d20，\u5c3e\u5220\u6700\u4e45\u672a\u7528\u3002<\/p>\n<\/li>\n

  • \n

    Q：Softmax \u68af\u5ea6\u600e\u4e48\u63a8\u5bfc？ A：\u8bbe y_i = e^{z_i} \/ \u03a3e^{z_j}，\u5219 \u2202L\/\u2202z_i = y_i – t_i（t_i \u4e3a one-hot \u6807\u7b7e）\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4e3a\u4ec0\u4e48\u4e0d\u7528 MSE \u505a\u5206\u7c7b\u635f\u5931？ A：MSE \u5bf9\u5f02\u5e38\u503c\u654f\u611f，\u4e14\u68af\u5ea6\u5728\u9884\u6d4b\u63a5\u8fd10\/1\u65f6\u8d8b\u4e8e0，\u5bfc\u81f4\u8bad\u7ec3\u7f13\u6162\u751a\u81f3\u505c\u6ede\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u6570\u636e\u4e0d\u5e73\u8861\u6709\u54ea\u4e9b\u5904\u7406\u65b9\u6cd5？ A：\u8fc7\u91c7\u6837（SMOTE）\u3001\u6b20\u91c7\u6837\u3001Focal Loss\u3001\u8c03\u6574\u5206\u7c7b\u9608\u503c\u3001\u96c6\u6210\u65b9\u6cd5（\u5982 BalancedRandomForest）\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u8bc4\u4f30\u591a\u6807\u7b7e\u5206\u7c7b\u6a21\u578b？ A：\u5e38\u7528\u6307\u6807：Hamming Loss（\u9519\u5206\u6bd4\u4f8b）\u3001Subset Accuracy（\u5168\u5bf9\u624d\u7b97\u5bf9）\u3001One-error（\u6700\u76f8\u5173\u6807\u7b7e\u4e0d\u5728\u771f\u5b9e\u96c6\u4e2d）\u3001F1-micro\/macro\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u7528\u4e24\u4e2a\u6808\u5b9e\u73b0\u4e00\u4e2a\u961f\u5217？ A：\u4f7f\u7528\u4e24\u4e2a\u6808 in_stack \u548c out_stack\u3002\u5165\u961f\u65f6\u538b\u5165 in_stack；\u51fa\u961f\u65f6，\u82e5 out_stack \u4e3a\u7a7a，\u5219\u5c06 in_stack \u5168\u90e8\u5f39\u51fa\u5e76\u538b\u5165 out_stack，\u518d\u4ece out_stack \u5f39\u51fa\u6808\u9876\u3002\u8fd9\u6837\u53ef\u4fdd\u8bc1 FIFO \u987a\u5e8f\u3002<\/p>\n<\/li>\n

  • \n

    Q：LeetCode 300 \u6700\u957f\u9012\u589e\u5b50\u5e8f\u5217（LIS）\u7684 O(n log n) \u89e3\u6cd5\u662f\u4ec0\u4e48？ A：\u7ef4\u62a4\u4e00\u4e2a\u6570\u7ec4 tails，\u5176\u4e2d tails[i] \u8868\u793a\u957f\u5ea6\u4e3a i+1 \u7684\u9012\u589e\u5b50\u5e8f\u5217\u7684\u6700\u5c0f\u672b\u5c3e\u5143\u7d20\u3002\u904d\u5386\u539f\u6570\u7ec4，\u5bf9\u6bcf\u4e2a\u5143\u7d20\u7528\u4e8c\u5206\u67e5\u627e\u627e\u5230\u7b2c\u4e00\u4e2a \u2265 \u5b83\u7684\u4f4d\u7f6e，\u66ff\u6362\u6216\u8ffd\u52a0\u3002\u6700\u7ec8 tails \u957f\u5ea6\u5373\u4e3a LIS \u957f\u5ea6\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4ec0\u4e48\u662f\u68af\u5ea6\u6d88\u5931？\u4e3a\u4ec0\u4e48 RNN \u5bb9\u6613\u51fa\u73b0\u68af\u5ea6\u6d88\u5931？ A：\u68af\u5ea6\u6d88\u5931\u6307\u53cd\u5411\u4f20\u64ad\u65f6\u68af\u5ea6\u9010\u5c42\u8870\u51cf\u8d8b\u8fd1\u4e8e 0，\u5bfc\u81f4\u6d45\u5c42\u53c2\u6570\u51e0\u4e4e\u4e0d\u66f4\u65b0\u3002RNN \u4e2d\u68af\u5ea6\u901a\u8fc7\u94fe\u5f0f\u6cd5\u5219\u8fde\u4e58\u591a\u4e2a Jacobian \u77e9\u9635，\u82e5\u5176\u7279\u5f81\u503c <1，\u591a\u6b21\u76f8\u4e58\u540e\u68af\u5ea6\u6307\u6570\u7ea7\u8870\u51cf\u3002<\/p>\n<\/li>\n

  • \n

    Q：LSTM \u5982\u4f55\u7f13\u89e3\u68af\u5ea6\u6d88\u5931？ A：LSTM \u5f15\u5165\u7ec6\u80de\u72b6\u6001（cell state）\u548c\u95e8\u63a7\u673a\u5236\u3002\u7ec6\u80de\u72b6\u6001\u901a\u8fc7\u201c\u6052\u7b49\u8def\u5f84\u201d\u4f20\u9012\u4fe1\u606f，\u9057\u5fd8\u95e8\u548c\u8f93\u5165\u95e8\u63a7\u5236\u4fe1\u606f\u6d41，\u4f7f\u68af\u5ea6\u53ef\u901a\u8fc7\u8fd1\u4f3c\u6052\u7b49\u6620\u5c04\u56de\u4f20，\u907f\u514d\u8fde\u4e58\u8870\u51cf\u3002<\/p>\n<\/li>\n

  • \n

    Q：Softmax \u51fd\u6570\u7684\u8f93\u51fa\u4e3a\u4f55\u4e0d\u9002\u5408\u4f5c\u4e3a\u6982\u7387\u6821\u51c6\u540e\u7684\u7f6e\u4fe1\u5ea6？ A：\u73b0\u4ee3\u6df1\u5ea6\u7f51\u7edc（\u5982 ResNet）\u7684 Softmax \u8f93\u51fa\u5f80\u5f80\u8fc7\u4e8e\u81ea\u4fe1（over-confident），\u5373\u9ad8 softmax \u503c \u2260 \u9ad8\u51c6\u786e\u7387\u3002\u8fd9\u662f\u7531\u4e8e\u6a21\u578b\u672a\u5145\u5206\u6b63\u5219\u5316\u6216\u8bad\u7ec3\u76ee\u6807\u672a\u663e\u5f0f\u4f18\u5316\u6821\u51c6\u5ea6，\u9700\u7528 Temperature Scaling \u7b49\u65b9\u6cd5\u6821\u51c6\u3002<\/p>\n<\/li>\n

  • \n

    Q：BatchNorm \u5728\u8bad\u7ec3\u548c\u63a8\u7406\u65f6\u6709\u4f55\u4e0d\u540c？ A：\u8bad\u7ec3\u65f6\u4f7f\u7528\u5f53\u524d batch \u7684\u5747\u503c\u548c\u65b9\u5dee\u8fdb\u884c\u5f52\u4e00\u5316，\u5e76\u8bb0\u5f55\u6ed1\u52a8\u5e73\u5747；\u63a8\u7406\u65f6\u4f7f\u7528\u8bad\u7ec3\u9636\u6bb5\u7d2f\u79ef\u7684\u5168\u5c40\u5747\u503c\u548c\u65b9\u5dee，\u4ee5\u4fdd\u8bc1\u786e\u5b9a\u6027\u8f93\u51fa\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u4e3a\u4ec0\u4e48 Transformer \u4f7f\u7528 LayerNorm \u800c\u4e0d\u662f BatchNorm？ A：\u56e0\u4e3a Transformer \u5904\u7406\u53d8\u957f\u5e8f\u5217，batch \u5185\u6837\u672c\u957f\u5ea6\u4e0d\u4e00，BatchNorm \u5bf9\u5c0f batch \u6216\u52a8\u6001\u957f\u5ea6\u4e0d\u7a33\u5b9a；LayerNorm \u5bf9\u5355\u4e2a\u6837\u672c\u6240\u6709\u7ef4\u5ea6\u5f52\u4e00\u5316，\u4e0e batch size \u65e0\u5173，\u9002\u5408 NLP \u4efb\u52a1\u3002<\/p>\n<\/li>\n

  • \n

    Q：BERT \u7684 [CLS] \u5411\u91cf\u4e3a\u4ec0\u4e48\u4e0d\u80fd\u76f4\u63a5\u7528\u4e8e\u53e5\u5411\u91cf\u76f8\u4f3c\u5ea6\u8ba1\u7b97？ A：\u56e0\u4e3a BERT \u7684 embedding \u7a7a\u95f4\u5b58\u5728\u5404\u5411\u5f02\u6027（anisotropy），[CLS] \u5411\u91cf\u5206\u5e03\u4e0d\u5747\u5300，\u9ad8\u9891\u8bcd\u805a\u96c6\u3001\u4f4e\u9891\u8bcd\u5206\u6563，\u5bfc\u81f4\u4f59\u5f26\u76f8\u4f3c\u5ea6\u5931\u771f，\u9700\u7528 Sentence-BERT \u6216 BERT-Flow \u6539\u8fdb\u3002<\/p>\n<\/li>\n

  • \n

    Q：Focal Loss \u7684\u6838\u5fc3\u601d\u60f3\u662f\u4ec0\u4e48？\u9002\u7528\u4e8e\u4ec0\u4e48\u573a\u666f？ A：\u901a\u8fc7 (1 – p_t)^\u03b3 \u964d\u4f4e\u6613\u5206\u7c7b\u6837\u672c\u7684\u6743\u91cd，\u4f7f\u6a21\u578b\u805a\u7126\u4e8e\u96be\u5206\u7c7b\u6837\u672c\u3002\u9002\u7528\u4e8e\u6b63\u8d1f\u6837\u672c\u6781\u5ea6\u4e0d\u5e73\u8861\u7684\u4efb\u52a1，\u5982\u76ee\u6807\u68c0\u6d4b\u4e2d\u7684\u524d\u666f\/\u80cc\u666f\u5206\u7c7b\u3002<\/p>\n<\/li>\n

  • \n

    Q：XGBoost \u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c？ A：\u5728\u8282\u70b9\u5206\u88c2\u65f6，XGBoost \u5c06\u7f3a\u5931\u503c\u6837\u672c\u5206\u522b\u5f52\u5165\u5de6\/\u53f3\u5b50\u6811，\u9009\u62e9\u4f7f\u635f\u5931\u4e0b\u964d\u66f4\u591a\u7684\u65b9\u5411\u4f5c\u4e3a\u9ed8\u8ba4\u5206\u88c2\u65b9\u5411，\u5e76\u5728\u9884\u6d4b\u65f6\u6cbf\u7528\u8be5\u89c4\u5219\u3002<\/p>\n<\/li>\n

  • \n

    Q：LightGBM \u7684 GOSS（Gradient-based One-Side Sampling）\u662f\u4ec0\u4e48？ A：GOSS \u4fdd\u7559\u68af\u5ea6\u7edd\u5bf9\u503c\u5927\u7684\u6837\u672c（\u91cd\u8981\u6837\u672c），\u5e76\u5bf9\u68af\u5ea6\u5c0f\u7684\u6837\u672c\u968f\u673a\u91c7\u6837，\u4ece\u800c\u5728\u51cf\u5c11\u8ba1\u7b97\u91cf\u7684\u540c\u65f6\u4fdd\u6301\u5bf9\u4fe1\u606f\u589e\u76ca\u7684\u51c6\u786e\u4f30\u8ba1，\u52a0\u901f\u8bad\u7ec3\u3002<\/p>\n<\/li>\n

  • \n

    Q：CatBoost \u5982\u4f55\u5904\u7406\u7c7b\u522b\u578b\u7279\u5f81？ A：CatBoost \u4f7f\u7528\u6709\u5e8f boosting（Ordered Boosting）\u548c\u57fa\u4e8e\u7edf\u8ba1\u7684\u7c7b\u522b\u7f16\u7801（\u5982 target encoding），\u5e76\u5728\u8bad\u7ec3\u65f6\u81ea\u52a8\u5904\u7406\u7c7b\u522b\u7279\u5f81，\u907f\u514d\u8fc7\u62df\u5408\u3002<\/p>\n<\/li>\n

  • \n

    Q：Wide & Deep \u6a21\u578b\u4e2d Wide \u90e8\u5206\u4e3a\u4ec0\u4e48\u8981\u4eba\u5de5\u6784\u9020\u4ea4\u53c9\u7279\u5f81？ A：\u56e0\u4e3a\u7ebf\u6027\u6a21\u578b\u65e0\u6cd5\u81ea\u52a8\u5b66\u4e60\u7279\u5f81\u7ec4\u5408，\u9700\u901a\u8fc7\u4eba\u5de5\u8bbe\u8ba1\u5982 AND(user_gender=female, item_category=cosmetics) \u6765\u6355\u6349\u9ad8\u9891\u5171\u73b0\u6a21\u5f0f，\u63d0\u5347\u8bb0\u5fc6\u80fd\u529b\u3002<\/p>\n<\/li>\n

  • \n

    Q：DeepFM \u76f8\u6bd4 Wide & Deep \u7684\u6700\u5927\u4f18\u52bf\u662f\u4ec0\u4e48？ A：DeepFM \u7528 FM \u66ff\u4ee3 Wide \u90e8\u5206，\u81ea\u52a8\u5b66\u4e60\u4e8c\u9636\u7279\u5f81\u4ea4\u53c9，\u65e0\u9700\u4eba\u5de5\u7279\u5f81\u5de5\u7a0b，\u4e14 FM \u4e0e Deep \u5171\u4eab embedding，\u8bad\u7ec3\u66f4\u9ad8\u6548\u3001\u6cdb\u5316\u66f4\u5f3a\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u5224\u65ad\u4e00\u4e2a\u94fe\u8868\u662f\u5426\u5b58\u5728\u73af？\u7a7a\u95f4\u590d\u6742\u5ea6 O(1) \u7684\u89e3\u6cd5？ A：\u4f7f\u7528\u5feb\u6162\u6307\u9488（Floyd \u5224\u5708\u7b97\u6cd5）\u3002\u5feb\u6307\u9488\u6bcf\u6b21\u8d70\u4e24\u6b65，\u6162\u6307\u9488\u6bcf\u6b21\u8d70\u4e00\u6b65，\u82e5\u76f8\u9047\u5219\u6709\u73af；\u82e5\u5feb\u6307\u9488\u5230\u8fbe null \u5219\u65e0\u73af\u3002\u7a7a\u95f4\u590d\u6742\u5ea6 O(1)\u3002<\/p>\n<\/li>\n

  • \n

    Q：LeetCode 141 \u73af\u5f62\u94fe\u8868\u7684\u5feb\u6162\u6307\u9488\u4e3a\u4ec0\u4e48\u4e00\u5b9a\u80fd\u76f8\u9047？ A：\u8bbe\u73af\u957f\u4e3a L，\u6162\u6307\u9488\u8fdb\u5165\u73af\u540e，\u5feb\u6307\u9488\u5df2\u5728\u73af\u5185\u3002\u4e24\u8005\u76f8\u5bf9\u901f\u5ea6\u4e3a 1，\u6700\u591a L \u6b65\u5185\u5feb\u6307\u9488\u4f1a\u8ffd\u4e0a\u6162\u6307\u9488，\u6545\u5fc5\u76f8\u9047\u3002<\/p>\n<\/li>\n

  • \n

    Q：TCP \u4e09\u6b21\u63e1\u624b\u7684\u8fc7\u7a0b\u662f\u4ec0\u4e48？\u4e3a\u4ec0\u4e48\u4e0d\u80fd\u4e24\u6b21？ A：\u8fc7\u7a0b：Client \u2192 SYN \u2192 Server；Server \u2192 SYN+ACK \u2192 Client；Client \u2192 ACK \u2192 Server\u3002\u4e24\u6b21\u63e1\u624b\u65e0\u6cd5\u9632\u6b62\u5386\u53f2\u8fde\u63a5\u8bf7\u6c42\u7a81\u7136\u5230\u8fbe\u670d\u52a1\u5668\u9020\u6210\u8d44\u6e90\u6d6a\u8d39（\u5982\u65e7 SYN \u91cd\u4f20）\u3002<\/p>\n<\/li>\n

  • \n

    Q：UDP \u4e3a\u4ec0\u4e48\u9002\u5408\u89c6\u9891\u6d41\u4f20\u8f93？ A：\u56e0\u4e3a UDP \u65e0\u8fde\u63a5\u3001\u65e0\u91cd\u4f20\u3001\u4f4e\u5ef6\u8fdf，\u5373\u4f7f\u4e22\u5305\u4e5f\u4e0d\u4f1a\u963b\u585e\u540e\u7eed\u6570\u636e，\u9002\u5408\u5bb9\u5fcd\u5c11\u91cf\u4e22\u5305\u4f46\u8981\u6c42\u5b9e\u65f6\u6027\u7684\u573a\u666f（\u5982\u76f4\u64ad\u3001\u89c6\u9891\u4f1a\u8bae）\u3002<\/p>\n<\/li>\n

  • \n

    Q：SIFT \u7279\u5f81\u4e3a\u4f55\u5177\u6709\u5c3a\u5ea6\u4e0d\u53d8\u6027？ A：SIFT \u5728\u9ad8\u65af\u91d1\u5b57\u5854（\u591a\u5c3a\u5ea6\u7a7a\u95f4）\u4e2d\u68c0\u6d4b\u6781\u503c\u70b9，\u901a\u8fc7 DoG（Difference of Gaussians）\u8fd1\u4f3c LoG，\u627e\u5230\u5728\u5c3a\u5ea6\u548c\u7a7a\u95f4\u4e0a\u90fd\u7a33\u5b9a\u7684\u5174\u8da3\u70b9，\u4ece\u800c\u5b9e\u73b0\u5c3a\u5ea6\u4e0d\u53d8\u3002<\/p>\n<\/li>\n

  • \n

    Q：LBP（\u5c40\u90e8\u4e8c\u503c\u6a21\u5f0f）\u5982\u4f55\u63d0\u53d6\u7eb9\u7406\u7279\u5f81？ A：\u4ee5\u50cf\u7d20\u4e3a\u4e2d\u5fc3，\u5c06\u5176 8 \u90bb\u57df\u7070\u5ea6\u4e0e\u4e2d\u5fc3\u6bd4\u8f83，\u5927\u4e8e\u4e3a 1，\u5426\u5219\u4e3a 0，\u5f62\u6210 8 \u4f4d\u4e8c\u8fdb\u5236\u6570\u4f5c\u4e3a LBP \u503c\u3002\u7edf\u8ba1\u6574\u56fe LBP \u76f4\u65b9\u56fe\u5373\u4e3a\u7eb9\u7406\u63cf\u8ff0\u3002<\/p>\n<\/li>\n

  • \n

    Q：HOG \u7279\u5f81\u7684\u8ba1\u7b97\u6b65\u9aa4\u662f\u4ec0\u4e48？ A：1）\u8ba1\u7b97\u56fe\u50cf\u68af\u5ea6\u5e45\u503c\u548c\u65b9\u5411；2）\u5212\u5206 cell（\u5982 8\u00d78），\u7edf\u8ba1\u68af\u5ea6\u65b9\u5411\u76f4\u65b9\u56fe；3）\u7ec4\u5408\u591a\u4e2a cell \u4e3a block（\u5982 2\u00d72），\u5bf9 block \u5185\u76f4\u65b9\u56fe\u505a L2 \u5f52\u4e00\u5316；4）\u62fc\u63a5\u6240\u6709 block \u7279\u5f81\u5411\u91cf\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u767d\u5e73\u8861\u7684\u7070\u5ea6\u4e16\u754c\u5047\u8bbe\u662f\u4ec0\u4e48？ A：\u5047\u8bbe\u81ea\u7136\u573a\u666f\u4e2d RGB \u4e09\u901a\u9053\u7684\u5e73\u5747\u503c\u8d8b\u4e8e\u76f8\u540c（\u5373\u6574\u4f53\u5448\u7070\u8272），\u636e\u6b64\u8c03\u6574\u5404\u901a\u9053\u589e\u76ca，\u4f7f\u56fe\u50cf\u5e73\u5747\u503c\u76f8\u7b49，\u6d88\u9664\u8272\u504f\u3002<\/p>\n<\/li>\n

  • \n

    Q：Adaboost \u548c\u968f\u673a\u68ee\u6797\u7684\u6838\u5fc3\u533a\u522b\u662f\u4ec0\u4e48？ A：Adaboost \u662f Boosting \u65b9\u6cd5，\u4e32\u884c\u8bad\u7ec3，\u6bcf\u8f6e\u5173\u6ce8\u524d\u4e00\u8f6e\u9519\u5206\u6837\u672c，\u5f31\u5206\u7c7b\u5668\u52a0\u6743\u6295\u7968；\u968f\u673a\u68ee\u6797\u662f Bagging \u65b9\u6cd5，\u5e76\u884c\u8bad\u7ec3，\u6bcf\u68f5\u6811\u72ec\u7acb\u91c7\u6837，\u6700\u7ec8\u591a\u6570\u6295\u7968\u3002<\/p>\n<\/li>\n

  • \n

    Q：KKT \u6761\u4ef6\u5728 SVM \u4e2d\u7684\u4f5c\u7528\u662f\u4ec0\u4e48？ A：KKT \u6761\u4ef6\u662f SVM \u5bf9\u5076\u95ee\u9898\u53d6\u5f97\u6700\u4f18\u89e3\u7684\u5145\u8981\u6761\u4ef6，\u5176\u4e2d\u5305\u542b\u4e92\u8865\u677e\u5f1b\u6761\u4ef6：\u03b1_i > 0 \u21d2 \u6837\u672c\u4e3a\u652f\u6301\u5411\u91cf；\u03b1_i = 0 \u21d2 \u6837\u672c\u4e0d\u5f71\u54cd\u51b3\u7b56\u8fb9\u754c\u3002<\/p>\n<\/li>\n

  • \n

    Q：ROC \u66f2\u7ebf\u548c PR \u66f2\u7ebf\u9002\u7528\u573a\u666f\u6709\u4f55\u4e0d\u540c？ A：ROC \u5bf9\u7c7b\u522b\u4e0d\u5e73\u8861\u4e0d\u654f\u611f，\u9002\u5408\u8bc4\u4f30\u6574\u4f53\u6392\u5e8f\u80fd\u529b；PR \u66f2\u7ebf（Precision-Recall）\u5bf9\u6b63\u6837\u672c\u7a00\u758f\u654f\u611f，\u66f4\u9002\u5408\u8bc4\u4f30\u5c11\u6570\u7c7b\u6027\u80fd（\u5982\u6b3a\u8bc8\u68c0\u6d4b）\u3002<\/p>\n<\/li>\n

  • \n

    Q：AUC \u7684\u7269\u7406\u610f\u4e49\u662f\u4ec0\u4e48？ A：AUC \u8868\u793a\u968f\u673a\u9009\u4e00\u4e2a\u6b63\u6837\u672c\u548c\u4e00\u4e2a\u8d1f\u6837\u672c，\u6a21\u578b\u7ed9\u6b63\u6837\u672c\u6253\u5206\u9ad8\u4e8e\u8d1f\u6837\u672c\u7684\u6982\u7387\u3002AUC=0.5 \u4e3a\u968f\u673a\u731c\u6d4b，AUC=1 \u4e3a\u5b8c\u7f8e\u5206\u7c7b\u3002<\/p>\n<\/li>\n

  • \n

    Q：TensorFlow 1.x \u4e2d Graph \u548c Session \u7684\u5173\u7cfb？ A：Graph \u5b9a\u4e49\u8ba1\u7b97\u6d41\u7a0b（\u9759\u6001\u56fe），Session \u8d1f\u8d23\u6267\u884c Graph \u5e76\u5206\u914d\u8d44\u6e90\u3002\u5148\u6784\u5efa Graph，\u518d\u901a\u8fc7 Session.run() \u8f93\u5165\u6570\u636e\u3001\u83b7\u53d6\u7ed3\u679c\u3002<\/p>\n<\/li>\n

  • \n

    Q：LeetCode 3 \u65e0\u91cd\u590d\u5b57\u7b26\u7684\u6700\u957f\u5b50\u4e32\u5982\u4f55\u7528\u6ed1\u52a8\u7a97\u53e3\u89e3\u51b3？ A：\u7ef4\u62a4\u7a97\u53e3 [left, right] \u548c\u54c8\u5e0c\u8868\u8bb0\u5f55\u5b57\u7b26\u6700\u8fd1\u4f4d\u7f6e\u3002right \u6269\u5c55\u65f6，\u82e5\u9047\u5230\u91cd\u590d\u5b57\u7b26，left \u8df3\u81f3\u5176\u4e0a\u6b21\u4f4d\u7f6e+1\u3002\u5168\u7a0b\u8bb0\u5f55\u7a97\u53e3\u6700\u5927\u957f\u5ea6\u3002<\/p>\n<\/li>\n

  • \n

    Q：\u5982\u4f55\u5728 1G \u5927\u6587\u4ef6\u4e2d\u627e\u51fa\u8bcd\u9891 Top 100（\u5185\u5b58\u4ec5 1M）？ A：1）Hash \u6620\u5c04：hash(word) % 5000 \u5206\u6210 5000 \u4e2a\u5c0f\u6587\u4ef6；2）\u5bf9\u6bcf\u4e2a\u5c0f\u6587\u4ef6\u7528 HashMap \u7edf\u8ba1\u8bcd\u9891；3）\u7528\u6700\u5c0f\u5806\u7ef4\u62a4 Top 100；4）\u5408\u5e76\u5404\u6587\u4ef6\u7ed3\u679c\u3002<\/p>\n<\/li>\n

  • \n

    Q：GBDT \u4e3a\u4ec0\u4e48\u4e0d\u80fd\u4f7f\u7528\u5e73\u65b9\u635f\u5931\u76f4\u63a5\u5904\u7406\u5206\u7c7b\u95ee\u9898？ A：\u53ef\u4ee5，\u4f46\u6548\u7387\u4f4e\u3002GBDT \u901a\u5e38\u5c06\u5206\u7c7b\u8f6c\u5316\u4e3a\u56de\u5f52\u95ee\u9898，\u7528\u5bf9\u6570\u635f\u5931（Log Loss）\u6216\u6307\u6570\u635f\u5931（AdaBoost）\u66f4\u5408\u9002，\u56e0\u5176\u68af\u5ea6\u66f4\u80fd\u53cd\u6620\u5206\u7c7b\u8bef\u5dee\u65b9\u5411\u3002<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"

    \u5927\u5382AI\u7b97\u6cd5\u9762\u8bd5\u9898\u6c47\u603b Q：\u5982\u4f55\u53cd\u8f6c\u4e00\u4e2a\u5355\u5411\u94fe\u8868？ A：\u4f7f\u7528\u53cc\u6307\u9488\u8fed\u4ee3\u6cd5\u3002\u521d\u59cb\u5316 pre None，cur head；\u904d\u5386\u94fe\u8868，\u6bcf\u6b21\u4fdd\u5b58 cur.next，\u5c06 cur.next \u6307\u5411 pre，\u7136\u540e\u66f4\u65b0 pre cur，cur nxt；\u6700\u7ec8\u8fd4\u56de pre\u3002 Q：\u5feb\u6392\u7684\u65f6\u95f4\u590d\u6742\u5ea6\u662f\u591a\u5c11？\u6700\u574f\u60c5\u51b5\u5982\u4f55\u89e6\u53d1&#<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,427],"topic":[],"class_list":["post-65873","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-427"],"yoast_head":"\n\u5927\u5382AI\u7b97\u6cd5\u9762\u8bd5\u9898\u6c47\u603b - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/65873.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5927\u5382AI\u7b97\u6cd5\u9762\u8bd5\u9898\u6c47\u603b - 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