{"id":189523,"date":"2024-05-09T17:41:23","date_gmt":"2024-05-09T09:41:23","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/189523.html"},"modified":"2024-05-09T17:41:26","modified_gmt":"2024-05-09T09:41:26","slug":"github-%e4%b8%8a%e6%9c%89%e5%93%aa%e4%ba%9b%e6%9c%89%e5%85%b3%e5%9b%be%e5%83%8f%e5%a4%84%e7%90%86%e6%88%96%e6%98%af%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e7%9a%84%e6%9c%89%e8%b6%a3%e9%a1%b9%e7%9b%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/189523.html","title":{"rendered":"GitHub \u4e0a\u6709\u54ea\u4e9b\u6709\u5173\u56fe\u50cf\u5904\u7406\u6216\u662f\u673a\u5668\u5b66\u4e60\u7684\u6709\u8da3\u9879\u76ee"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/26095507\/731a98ad-9219-4997-93f3-e3aa3e4b4bdd.webp\" alt=\"GitHub \u4e0a\u6709\u54ea\u4e9b\u6709\u5173\u56fe\u50cf\u5904\u7406\u6216\u662f\u673a\u5668\u5b66\u4e60\u7684\u6709\u8da3\u9879\u76ee\" \/><\/p>\n<p><p>GitHub\u4e0a\u5173\u4e8e\u56fe\u50cf\u5904\u7406\u548c<a href=\"https:\/\/docs.pingcode.com\/ask\/59192.html\" target=\"_blank\">\u673a\u5668\u5b66\u4e60<\/a>\u7684\u6709\u8da3\u9879\u76ee\u4f17\u591a\uff0c<strong>\u8fd9\u4e9b\u9879\u76ee\u4e3b\u8981\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\uff1a\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5b9e\u73b0\u3001\u56fe\u50cf\u8bc6\u522b\u6280\u672f\u3001\u56fe\u50cf\u751f\u6210\u6280\u672f\u3001\u548c\u673a\u5668\u89c6\u89c9\u5e94\u7528<\/strong>\u3002\u5728\u8fd9\u5176\u4e2d\uff0c<strong>\u56fe\u50cf\u8bc6\u522b\u6280\u672f<\/strong>\u662f\u975e\u5e38\u6838\u5fc3\u4e14\u5e7f\u6cdb\u5e94\u7528\u7684\u4e00\u73af\uff0c\u5b83\u901a\u8fc7\u8bad\u7ec3\u6a21\u578b\u8bc6\u522b\u548c\u7406\u89e3\u56fe\u7247\u7684\u5185\u5bb9\uff0c\u4ece\u800c\u5b9e\u73b0\u4ece\u7b80\u5355\u7684\u7269\u4f53\u8bc6\u522b\u5230\u590d\u6742\u573a\u666f\u7406\u89e3\u7684\u5404\u79cd\u529f\u80fd\u3002\u8fd9\u4e00\u6280\u672f\u7684\u5b9e\u73b0\u901a\u5e38\u4f9d\u8d56\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\uff0c\u7279\u522b\u662f\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u5728\u5185\u7684\u4e00\u7cfb\u5217\u7b97\u6cd5\uff0c\u8fd9\u4e9b\u7b97\u6cd5\u80fd\u591f\u4ece\u5927\u91cf\u7684\u56fe\u50cf\u6570\u636e\u4e2d\u5b66\u4e60\u5230\u6709\u7528\u7684\u7279\u5f81\u8868\u793a\uff0c\u4ece\u800c\u63d0\u9ad8\u8bc6\u522b\u7684\u51c6\u786e\u6027\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n<p><p>\u63a5\u4e0b\u6765\uff0c\u5c06\u4ecb\u7ecd\u4e00\u4e9b\u5728GitHub\u4e0a\u53d7\u5230\u5e7f\u6cdb\u5173\u6ce8\u4e14\u5bcc\u6709\u521b\u610f\u7684\u56fe\u50cf\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u9879\u76ee\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001DEEP LEARNING MODELS<\/h3>\n<\/p>\n<p><p><strong>\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/strong>\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u4e00\u5927\u70ed\u70b9\uff0c\u5c24\u5176\u662f\u5728\u56fe\u50cf\u5904\u7406\u9886\u57df\u3002\u5176\u4e2d\uff0cTensorFlow\u548cPyTorch\u662f\u4e24\u4e2a\u975e\u5e38\u53d7\u6b22\u8fce\u7684\u5f00\u6e90\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u5b83\u4eec\u4e3a\u7814\u7a76\u8005\u548c\u5f00\u53d1\u8005\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u8ba1\u7b97\u80fd\u529b\uff0c\u4ee5\u53ca\u65b9\u4fbf\u5feb\u6377\u7684\u6a21\u578b\u642d\u5efa\u3001\u8bad\u7ec3\u548c\u90e8\u7f72\u7684\u63a5\u53e3\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p>TensorFlow Models\uff1a\u8fd9\u662f\u4e00\u4e2a\u7531Google\u5f00\u53d1\u7684\u5f00\u6e90\u5e93\uff0c\u91cc\u9762\u6c47\u96c6\u4e86\u5404\u79cd\u5404\u6837\u7684\u6a21\u578b\u548c\u7b97\u6cd5\u5b9e\u73b0\uff0c\u6db5\u76d6\u4e86\u56fe\u50cf\u8bc6\u522b\u3001\u6587\u672c\u5206\u7c7b\u7b49\u591a\u79cd\u4efb\u52a1\u3002\u8fd9\u4e2a\u9879\u76ee\u4e0d\u65ad\u66f4\u65b0\uff0c\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6559\u7a0b\u548c\u6848\u4f8b\uff0c\u975e\u5e38\u9002\u5408\u521d\u5b66\u8005\u5b66\u4e60\u548c\u4f7f\u7528\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p>PyTorch Tutorials\uff1a\u5bf9\u4e8e\u90a3\u4e9b\u559c\u6b22\u4f7f\u7528PyTorch\u7684\u5f00\u53d1\u8005\u6765\u8bf4\uff0c\u8fd9\u4e2a\u9879\u76ee\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u6559\u7a0b\u548c\u793a\u4f8b\u4ee3\u7801\uff0c\u8986\u76d6\u4e86\u4ece\u57fa\u7840\u77e5\u8bc6\u5230\u8fdb\u9636\u6280\u80fd\u7684\u5168\u9762\u5185\u5bb9\u3002\u5176\u4e2d\u4e0d\u4e4f\u9ad8\u8d28\u91cf\u7684\u56fe\u50cf\u5904\u7406\u6a21\u578b\uff0c\u5982\u56fe\u50cf\u5206\u7c7b\u3001\u56fe\u50cf\u751f\u6210\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n<\/ul>\n<p><h3>\u4e8c\u3001IMAGE RECOGNITION TECHNOLOGY<\/h3>\n<\/p>\n<p><p>\u56fe\u50cf\u8bc6\u522b\u6280\u672f\u5728\u673a\u5668\u5b66\u4e60\u9886\u57df\u626e\u6f14\u7740\u91cd\u8981\u89d2\u8272\uff0c\u5b83\u80fd\u591f\u52a9\u529b\u8ba1\u7b97\u673a\u89c6\u89c9\u7cfb\u7edf\u7406\u89e3\u56fe\u50cf\u5185\u5bb9\u3002OpenCV\u662f\u5728\u8fd9\u4e00\u9886\u57df\u5e94\u7528\u975e\u5e38\u5e7f\u6cdb\u7684\u4e00\u4e2a\u9879\u76ee\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p>OpenCV\uff1a\u4f5c\u4e3a\u4e00\u4e2a\u5f00\u6e90\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u673a\u5668\u5b66\u4e60\u8f6f\u4ef6\u5e93\uff0cOpenCV\u63d0\u4f9b\u4e86\u4e0a\u5343\u79cd\u4e0d\u540c\u7684\u7b97\u6cd5\uff0c\u8fd9\u4e9b\u7b97\u6cd5\u53ef\u4ee5\u5e94\u7528\u5728\u4eba\u8138\u8bc6\u522b\u3001\u7269\u4f53\u68c0\u6d4b\u3001\u56fe\u50cf\u8fc7\u6ee4\u7b49\u591a\u79cd\u573a\u666f\u4e2d\u3002\u5b83\u652f\u6301C++\u3001Python\u7b49\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\uff0c\u9002\u5408\u4e0d\u540c\u5c42\u6b21\u7684\u5f00\u53d1\u8005\u4f7f\u7528\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p>YOLO\uff08You Only Look Once\uff09\uff1a\u8fd9\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u5b9e\u65f6\u5bf9\u8c61\u68c0\u6d4b\u7cfb\u7edf\uff0c\u4ee5\u5176\u9ad8\u901f\u5ea6\u548c\u51c6\u786e\u7387\u800c\u95fb\u540d\u3002YOLO\u5c06\u7269\u4f53\u68c0\u6d4b\u7684\u4efb\u52a1\u901a\u8fc7\u5355\u4e00\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u5b9e\u73b0\uff0c\u80fd\u591f\u5728\u56fe\u50cf\u4e2d\u5feb\u901f\u51c6\u786e\u5730\u8bc6\u522b\u548c\u5206\u7c7b\u591a\u4e2a\u5bf9\u8c61\u3002<\/p>\n<\/p>\n<\/li>\n<\/ul>\n<p><h3>\u4e09\u3001IMAGE GENERATION TECHNOLOGY<\/h3>\n<\/p>\n<p><p>\u56fe\u50cf\u751f\u6210\u6280\u672f\u662f\u4e00\u4e2a\u7814\u7a76\u5982\u4f55\u8ba9\u673a\u5668\u81ea\u4e3b\u4ea7\u751f\u56fe\u7247\u7684\u9886\u57df\uff0cGANs\uff08\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff09\u662f\u5176\u4e2d\u7684\u4e00\u4e2a\u4eae\u70b9\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p>StyleGAN\uff1a\u8fd9\u662fNVIDIA\u5f00\u53d1\u7684\u4e00\u4e2a\u9879\u76ee\uff0c\u4f7f\u7528GANs\u751f\u6210\u9ad8\u5206\u8fa8\u7387\u3001\u903c\u771f\u7684\u4eba\u8138\u56fe\u50cf\u3002StyleGAN\u7684\u7279\u70b9\u662f\u80fd\u901a\u8fc7\u8c03\u6574\u4e0d\u540c\u7684\u53c2\u6570\u6765\u63a7\u5236\u751f\u6210\u56fe\u50cf\u7684\u5404\u79cd\u5c5e\u6027\uff0c\u5982\u5e74\u9f84\u3001\u5934\u53d1\u98ce\u683c\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p>DALL\u00b7E\uff1a\u7531Open<a href=\"https:\/\/docs.pingcode.com\/blog\/59162.html\" target=\"_blank\">AI<\/a>\u5f00\u53d1\uff0c\u80fd\u591f\u6839\u636e\u6587\u672c\u63cf\u8ff0\u751f\u6210\u5bf9\u5e94\u7684\u56fe\u50cf\u3002DALL\u00b7E\u7684\u521b\u65b0\u4e4b\u5904\u5728\u4e8e\u5176\u5bf9\u8bed\u8a00\u548c\u89c6\u89c9\u4fe1\u606f\u7684\u7406\u89e3\uff0c\u53ef\u4ee5\u4ea7\u751f\u4e30\u5bcc\u4e14\u591a\u6837\u5316\u7684\u56fe\u50cf\u5185\u5bb9\uff0c\u5c55\u793a\u4e86<a href=\"https:\/\/docs.pingcode.com\/tag\/AI\" target=\"_blank\">\u4eba\u5de5\u667a\u80fd<\/a>\u5728\u521b\u610f\u9886\u57df\u7684\u5de8\u5927\u6f5c\u529b\u3002<\/p>\n<\/p>\n<\/li>\n<\/ul>\n<p><h3>\u56db\u3001MACHINE VISION APPLICATIONS<\/h3>\n<\/p>\n<p><p>\u673a\u5668\u89c6\u89c9\u5e94\u7528\u662f\u6307\u5c06\u56fe\u50cf\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u6280\u672f\u5e94\u7528\u4e8e\u5b9e\u9645\u95ee\u9898\u89e3\u51b3\u7684\u9879\u76ee\uff0c\u4f8b\u5982\u81ea\u52a8\u9a7e\u9a76\u3001\u533b\u7597\u56fe\u50cf\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n<ul>\n<li>\n<p>Autonomous Driving\uff1a\u81ea\u52a8\u9a7e\u9a76\u6280\u672f\u9700\u8981\u5904\u7406\u5927\u91cf\u7684\u89c6\u89c9\u4fe1\u606f\uff0c\u4ee5\u786e\u4fdd\u8f66\u8f86\u80fd\u591f\u5b89\u5168\u5730\u9a7e\u9a76\u3002GitHub\u4e0a\u6709\u8bb8\u591a\u76f8\u5173\u9879\u76ee\uff0c\u6d89\u53ca\u5230\u8f66\u9053\u7ebf\u68c0\u6d4b\u3001\u4ea4\u901a\u6807\u8bc6\u8bc6\u522b\u7b49\u5173\u952e\u6280\u672f\u3002<\/p>\n<\/p>\n<\/li>\n<li>\n<p>Medical Image Analysis\uff1a\u533b\u7597\u56fe\u50cf\u5206\u6790\u662f\u53e6\u4e00\u4e2a\u91cd\u8981\u5e94\u7528\u9886\u57df\uff0c\u901a\u8fc7\u5206\u6790X\u5149\u3001MRI\u7b49\u533b\u7597\u56fe\u50cf\uff0c\u53ef\u4ee5\u5e2e\u52a9\u533b\u751f\u8bca\u65ad\u75be\u75c5\u3002\u8fd9\u4e9b\u9879\u76ee\u901a\u5e38\u4f1a\u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\u6765\u63d0\u9ad8\u8bca\u65ad\u7684\u51c6\u786e\u7387\u548c\u6548\u7387\u3002<\/p>\n<\/p>\n<\/li>\n<\/ul>\n<p><p>\u901a\u8fc7\u8fd9\u4e9b\u9879\u76ee\u7684\u5b66\u4e60\u548c\u7814\u7a76\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u4e2a\u4eba\u7684\u6280\u672f\u6c34\u5e73\uff0c\u4e5f\u80fd\u591f\u5bf9\u673a\u5668\u5b66\u4e60\u548c\u56fe\u50cf\u5904\u7406\u7684\u6700\u65b0\u53d1\u5c55\u52a8\u6001\u6709\u66f4\u6df1\u5165\u7684\u4e86\u89e3\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p><strong>1. \u5982\u4f55\u5728GitHub\u4e0a\u627e\u5230\u6709\u5173\u56fe\u50cf\u5904\u7406\u6216\u673a\u5668\u5b66\u4e60\u7684\u6709\u8da3\u9879\u76ee\uff1f<\/strong><br \/>\nGitHub\u4f5c\u4e3a\u4e00\u4e2a\u5168\u7403\u6700\u5927\u7684\u5f00\u6e90\u4ee3\u7801\u6258\u7ba1\u5e73\u53f0\uff0c\u4e0a\u9762\u6709\u8bb8\u591a\u4e0e\u56fe\u50cf\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u76f8\u5173\u7684\u4ee4\u4eba\u5174\u594b\u7684\u9879\u76ee\u3002\u4e3a\u4e86\u627e\u5230\u8fd9\u4e9b\u9879\u76ee\uff0c\u60a8\u53ef\u4ee5\u9075\u5faa\u4ee5\u4e0b\u6b65\u9aa4\uff1a<br \/>\na) \u5728GitHub\u4e3b\u9875\u7684\u641c\u7d22\u6846\u4e2d\u8f93\u5165\u5173\u952e\u8bcd\uff0c\u4f8b\u5982&quot;\u56fe\u50cf\u5904\u7406&quot;\u6216&quot;\u673a\u5668\u5b66\u4e60&quot;\u3002<br \/>\nb) \u6392\u5e8f\u7ed3\u679c\u53ef\u4ee5\u6309\u7167\u6700\u53d7\u6b22\u8fce\u3001\u6700\u65b0\u66f4\u65b0\u6216\u661f\u7ea7\u8fdb\u884c\u3002\u8fd9\u4e9b\u6307\u6807\u53ef\u4ee5\u5e2e\u52a9\u60a8\u627e\u5230\u6700\u6709\u8da3\u7684\u9879\u76ee\u3002<br \/>\nc) \u6d4f\u89c8\u641c\u7d22\u7ed3\u679c\u65f6\uff0c\u53ef\u4ee5\u67e5\u770b\u9879\u76ee\u7684README\u6587\u4ef6\u548c\u4ee3\u7801\uff0c\u4ee5\u786e\u5b9a\u662f\u5426\u7b26\u5408\u60a8\u7684\u5174\u8da3\u3002<br \/>\nd) \u8fd8\u53ef\u4ee5\u9605\u8bfb\u9879\u76ee\u7684\u8bb8\u53ef\u8bc1\u548c\u8d21\u732e\u6307\u5357\uff0c\u4ee5\u4e86\u89e3\u9879\u76ee\u662f\u5426\u9002\u5408\u60a8\u7684\u9700\u6c42\u548c\u53c2\u4e0e\u5ea6\u3002<\/p>\n<p><strong>2.\u6709\u54ea\u4e9b\u5728GitHub\u4e0a\u5907\u53d7\u6b22\u8fce\u7684\u56fe\u50cf\u5904\u7406\u6216\u673a\u5668\u5b66\u4e60\u9879\u76ee\uff1f<\/strong><br \/>\nGitHub\u4e0a\u6709\u8bb8\u591a\u5907\u53d7\u6b22\u8fce\u7684\u56fe\u50cf\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u9879\u76ee\uff0c\u4ee5\u4e0b\u662f\u5176\u4e2d\u4e00\u4e9b\u4ee4\u4eba\u5174\u594b\u7684\u9879\u76ee\uff1a<\/p>\n<ul>\n<li>OpenCV: \u8fd9\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5177\u6709\u4e30\u5bcc\u7684\u56fe\u50cf\u5904\u7406\u529f\u80fd\u548c\u7b97\u6cd5\u3002<\/li>\n<li>TensorFlow: \u8fd9\u662f\u4e00\u4e2a\u6d41\u884c\u7684\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u652f\u6301\u5404\u79cd\u6df1\u5ea6\u5b66\u4e60\u4efb\u52a1\uff0c\u5982\u56fe\u50cf\u5206\u7c7b\u3001\u76ee\u6807\u68c0\u6d4b\u548c\u8bed\u97f3\u8bc6\u522b\u3002<\/li>\n<li>PyTorch: \u8fd9\u662f\u53e6\u4e00\u4e2a\u6d41\u884c\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u4ee5\u5176\u52a8\u6001\u56fe\u5f62\u6784\u5efa\u548c\u7075\u6d3b\u6027\u800c\u95fb\u540d\uff0c\u88ab\u5e7f\u6cdb\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u9879\u76ee\u3002<\/li>\n<li>CycleGAN: \u8fd9\u662f\u4e00\u4e2a\u6709\u8da3\u7684\u9879\u76ee\uff0c\u7528\u4e8e\u56fe\u50cf\u98ce\u683c\u8f6c\u6362\uff0c\u5b83\u53ef\u4ee5\u5c06\u4e00\u79cd\u7c7b\u578b\u7684\u56fe\u50cf\u98ce\u683c\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u7c7b\u578b\u7684\u56fe\u50cf\u98ce\u683c\uff0c\u4f8b\u5982\u5c06\u9a6c\u7684\u56fe\u50cf\u8f6c\u6362\u4e3a\u6591\u9a6c\u7684\u56fe\u50cf\u3002<\/li>\n<li>Fast Style Transfer: \u8fd9\u4e2a\u9879\u76ee\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u5b9e\u73b0\u4e86\u5feb\u901f\u7684\u56fe\u50cf\u98ce\u683c\u8fc1\u79fb\uff0c\u53ef\u4ee5\u5c06\u4e00\u79cd\u827a\u672f\u98ce\u683c\u5e94\u7528\u4e8e\u4efb\u610f\u56fe\u50cf\u3002<\/li>\n<\/ul>\n<p><strong>3. \u6211\u5982\u4f55\u8d21\u732e\u81ea\u5df1\u7684\u56fe\u50cf\u5904\u7406\u6216\u673a\u5668\u5b66\u4e60\u9879\u76ee\u5230GitHub\uff1f<\/strong><br \/>\n\u5982\u679c\u60a8\u6709\u81ea\u5df1\u7684\u56fe\u50cf\u5904\u7406\u6216\u673a\u5668\u5b66\u4e60\u9879\u76ee\uff0c\u5e76\u5e0c\u671b\u8d21\u732e\u5230GitHub\u4e0a\u4e0e\u5176\u4ed6\u4eba\u5171\u4eab\uff0c\u60a8\u53ef\u4ee5\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u8fdb\u884c\u64cd\u4f5c\uff1a<br \/>\na) \u5728GitHub\u4e0a\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u4ed3\u5e93\uff0c\u7528\u4e8e\u6258\u7ba1\u60a8\u7684\u9879\u76ee\u4ee3\u7801\u548c\u76f8\u5173\u6587\u4ef6\u3002<br \/>\nb) \u7f16\u5199\u9879\u76ee\u7684README\u6587\u4ef6\uff0c\u5305\u62ec\u9879\u76ee\u6982\u8ff0\u3001\u5b89\u88c5\u8bf4\u660e\u3001\u4f7f\u7528\u65b9\u6cd5\u548c\u793a\u4f8b\u7b49\u3002<br \/>\nc) \u5c06\u9879\u76ee\u7684\u4ee3\u7801\u4e0a\u4f20\u5230\u60a8\u7684GitHub\u4ed3\u5e93\u4e2d\uff0c\u5e76\u4f7f\u7528Git\u8fdb\u884c\u7248\u672c\u63a7\u5236\u3002<br \/>\nd) \u6dfb\u52a0\u9002\u5f53\u7684\u8bb8\u53ef\u8bc1\u6587\u4ef6\uff0c\u4ee5\u660e\u786e\u60a8\u7684\u9879\u76ee\u7684\u4f7f\u7528\u548c\u5206\u53d1\u6761\u6b3e\u3002<br \/>\ne) \u5982\u679c\u60a8\u5e0c\u671b\u5176\u4ed6\u4eba\u53c2\u4e0e\u8d21\u732e\uff0c\u60a8\u53ef\u4ee5\u6dfb\u52a0\u8d21\u732e\u6307\u5357\uff0c\u5e76\u6b22\u8fce\u5176\u4ed6\u5f00\u53d1\u8005\u63d0\u4ea4\u95ee\u9898\u548c\u62c9\u53d6\u8bf7\u6c42\u3002<\/p>\n<p>\u8bb0\u4f4f\uff0c\u5c06\u60a8\u7684\u9879\u76ee\u4e0a\u4f20\u5230GitHub\u4e0a\u5c06\u4f7f\u60a8\u7684\u9879\u76ee\u5f97\u5230\u66f4\u591a\u4eba\u7684\u5173\u6ce8\u548c\u8d21\u732e\uff0c\u540c\u65f6\u4e5f\u4e3a\u60a8\u63d0\u4f9b\u4e86\u5e7f\u6cdb\u7684\u53ef\u89c6\u5316\u548c\u534f\u4f5c\u5de5\u5177\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"GitHub\u4e0a\u5173\u4e8e\u56fe\u50cf\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u7684\u6709\u8da3\u9879\u76ee\u4f17\u591a\uff0c\u8fd9\u4e9b\u9879\u76ee\u4e3b\u8981\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\uff1a\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7684\u5b9e\u73b0\u3001\u56fe\u50cf\u8bc6\u522b\u6280\u672f [&hellip;]","protected":false},"author":3,"featured_media":189524,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/189523"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=189523"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/189523\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/189524"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=189523"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=189523"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=189523"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}