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\u4e8c\u3001\u56de\u5f52<\/h3>\n<\/p>\n
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\u4e09\u3001\u805a\u7c7b<\/h3>\n<\/p>\n
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\u56db\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09<\/h3>\n<\/p>\n
\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5141\u8bb8\u8ba1\u7b97\u673a\u7406\u89e3\u3001\u89e3\u91ca\u548c\u751f\u6210\u4eba\u7c7b\u8bed\u8a00\u3002Python\u5728NLP\u65b9\u9762\u62e5\u6709\u4e30\u5bcc\u7684\u5e93\uff0c\u5982NLTK\u3001Spacy\u548cGensim<\/strong>\uff0c\u5b83\u4eec\u63d0\u4f9b\u4e86\u4ece\u57fa\u672c\u7684\u6587\u672c\u5904\u7406\u529f\u80fd\u5230\u590d\u6742\u7684\u8bed\u4e49\u5206\u6790\u5de5\u5177\u3002<\/p>\n<\/p>\nNLP\u4efb\u52a1\u5305\u62ec\u60c5\u611f\u5206\u6790\u3001\u4e3b\u9898\u5efa\u6a21\u3001\u673a\u5668\u7ffb\u8bd1\u548c\u804a\u5929\u673a\u5668\u4eba\u5f00\u53d1\u3002Python\u901a\u8fc7\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff08\u4f8b\u5982TensorFlow\u548cPytorch\uff09\u548c\u9884\u8bad\u7ec3\u6a21\u578b\uff08\u5982BERT\u3001GPT-2\uff09\u53ef\u4ee5\u5904\u7406\u9ad8\u7ea7NLP\u4efb\u52a1\uff0c\u5141\u8bb8\u5b66\u4e60\u548c\u63a8\u7406\u590d\u6742\u7684\u8bed\u8a00\u6a21\u5f0f\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u8ba1\u7b97\u673a\u89c6\u89c9<\/h3>\n<\/p>\n
\u8ba1\u7b97\u673a\u89c6\u89c9\u662f\u673a\u5668\u5b66\u4e60\u7684\u4e00\u4e2a\u5b50\u96c6\uff0c\u5b83\u4f7f\u8ba1\u7b97\u673a\u80fd\u591f\u4ece\u56fe\u7247\u6216\u89c6\u9891\u4e2d\u63d0\u53d6\u6709\u7528\u4fe1\u606f\u548c\u8bc6\u522b\u5bf9\u8c61\u3002\u4f7f\u7528Python\u7684\u5e93\uff0c\u5982OpenCV\u548cPillow\u8fdb\u884c\u56fe\u50cf\u9884\u5904\u7406\u548c\u7279\u5f81\u63d0\u53d6\uff0c\u800c\u6df1\u5ea6\u5b66\u4e60\u5e93\u5982Keras\u548cTensorFlow\u5219\u7528\u4e8e\u5b9e\u73b0\u590d\u6742\u7684\u56fe\u50cf\u8bc6\u522b\u4e0e\u5904\u7406\u4efb\u52a1<\/strong>\u3002<\/p>\n<\/p>\n\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u5e94\u7528\u5305\u62ec\u9762\u90e8\u8bc6\u522b\u3001\u81ea\u52a8\u9a7e\u9a76\u8f66\u8f86\u3001\u624b\u52bf\u8bc6\u522b\u4ee5\u53ca\u901a\u8fc7CT\u626b\u63cf\u6216MRI\u56fe\u50cf\u8fdb\u884c\u533b\u5b66\u8bca\u65ad\u7b49\u3002<\/p>\n<\/p>\n
\u516d\u3001\u63a8\u8350\u7cfb\u7edf<\/h3>\n<\/p>\n
\u63a8\u8350\u7cfb\u7edf\u662f\u4fe1\u606f\u8fc7\u6ee4\u7cfb\u7edf\uff0c\u65e8\u5728\u9884\u6d4b\u7528\u6237\u53ef\u80fd\u5bf9\u5546\u54c1\u7684\u504f\u597d\u7a0b\u5ea6\u3002\u5728Python\u4e2d\uff0c\u63a8\u8350\u7cfb\u7edf\u53ef\u4ee5\u901a\u8fc7\u534f\u540c\u8fc7\u6ee4\u3001\u5185\u5bb9\u63a8\u8350\u548c\u6df7\u5408\u65b9\u6cd5\u6784\u5efa\u3002\u5e93\uff0c\u5982Surprise\u548cscikit-learn\u63d0\u4f9b\u4e86\u5b9e\u73b0\u63a8\u8350\u7b97\u6cd5\u6240\u9700\u7684\u5de5\u5177<\/strong>\u3002<\/p>\n<\/p>\n\u63a8\u8350\u7cfb\u7edf\u6df1\u523b\u5730\u6539\u53d8\u4e86\u6211\u4eec\u7684\u5728\u7ebf\u4f53\u9a8c\uff0c\u4ece\u7535\u5f71\u63a8\u8350\uff08\u5982Netflix\uff09\u5230\u5546\u54c1\u63a8\u8350\uff08\u6bd4\u5982\u4e9a\u9a6c\u900a\uff09\u7b49\u5404\u4e2a\u9886\u57df\u90fd\u6709\u5e94\u7528\u3002<\/p>\n<\/p>\n
Python\u4e4b\u6240\u4ee5\u9002\u5408\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u4e00\u65b9\u9762\u662f\u7531\u4e8e\u5176\u5305\u542b\u4e30\u5bcc\u7684\u4e13\u95e8\u5e93\uff0c\u53e6\u4e00\u65b9\u9762\u5219\u56e0\u4e3a\u5176\u4ee3\u7801\u53ef\u8bfb\u6027\u597d\uff0c\u6613\u4e8e\u7f16\u5199\u548c\u7ef4\u62a4<\/strong>\u3002\u540c\u65f6\uff0cPython\u8fd8\u6709\u7740\u5e9e\u5927\u7684\u5f00\u53d1\u8005\u793e\u533a\uff0c\u4e3a\u89e3\u51b3\u5404\u79cd\u673a\u5668\u5b66\u4e60\u76f8\u5173\u7684\u95ee\u9898\u63d0\u4f9b\u4e86\u5927\u91cf\u7684\u8d44\u6e90\u548c\u652f\u6301\u3002\u8fd9\u4f7f\u5f97Python\u6210\u4e3a\u673a\u5668\u5b66\u4e60\u9886\u57df\u7814\u7a76\u4eba\u5458\u548c\u4ece\u4e1a\u8005\u7684\u9996\u9009\u8bed\u8a00\u3002<\/p>\n<\/p>\n\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\nPython\u9002\u7528\u4e8e\u54ea\u4e9b\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff1f<\/strong><\/p>\n\n- \n
Python\u9002\u7528\u4e8e\u6570\u636e\u9884\u5904\u7406\u548c\u6e05\u7406\u3002<\/strong> \u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u6570\u636e\u901a\u5e38\u9700\u8981\u8fdb\u884c\u9884\u5904\u7406\u548c\u6e05\u6d17\uff0c\u4ee5\u53bb\u9664\u566a\u97f3\u3001\u5904\u7406\u7f3a\u5931\u503c\u6216\u5f02\u5e38\u503c\u3002Python\u7684\u5e93\uff08\u5982NumPy\u548cPandas\uff09\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u7528\u4e8e\u8f6c\u6362\u3001\u8fc7\u6ee4\u548c\u6e05\u7406\u6570\u636e\u3002<\/p>\n<\/li>\n- \n
Python\u9002\u7528\u4e8e\u7279\u5f81\u9009\u62e9\u548c\u63d0\u53d6\u3002<\/strong> \u7279\u5f81\u9009\u62e9\u548c\u63d0\u53d6\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u6b65\u9aa4\uff0c\u5b83\u4eec\u6709\u52a9\u4e8e\u9009\u62e9\u6700\u76f8\u5173\u7684\u7279\u5f81\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3a\u9002\u5408\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u5f62\u5f0f\u3002Python\u4e2d\u7684\u5e93\uff08\u5982Scikit-learn\u548cFeaturetools\uff09\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u53ef\u7528\u4e8e\u7279\u5f81\u9009\u62e9\u3001\u7279\u5f81\u63d0\u53d6\u548c\u7279\u5f81\u5de5\u7a0b\u3002<\/p>\n<\/li>\n- \n
Python\u9002\u7528\u4e8e\u5efa\u7acb\u548c\u8bad\u7ec3\u6a21\u578b\u3002<\/strong> Python\u62e5\u6709\u5e7f\u6cdb\u7684\u673a\u5668\u5b66\u4e60\u5e93\u548c\u6846\u67b6\uff0c\u5982Scikit-learn\u3001TensorFlow\u548cPyTorch\uff0c\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7b97\u6cd5\u548c\u6a21\u578b\uff0c\u53ef\u7528\u4e8e\u5efa\u7acb\u548c\u8bad\u7ec3\u5404\u79cd\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u5305\u62ec\u56de\u5f52\u3001\u5206\u7c7b\u3001\u805a\u7c7b\u548c\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u7b49\u3002<\/p>\n<\/li>\n- \n
Python\u9002\u7528\u4e8e\u6a21\u578b\u8bc4\u4f30\u548c\u8c03\u4f18\u3002<\/strong> \u4e00\u65e6\u6a21\u578b\u88ab\u8bad\u7ec3\u51fa\u6765\uff0cPython\u7684\u673a\u5668\u5b66\u4e60\u5e93\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u6307\u6807\u548c\u65b9\u6cd5\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff0c\u5e76\u901a\u8fc7\u8c03\u4f18\u53c2\u6570\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u9c81\u68d2\u6027\u3002<\/p>\n<\/li>\n<\/ol>\n\u4e3a\u4ec0\u4e48Python\u9002\u7528\u4e8e\u8fd9\u4e9b\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff1f<\/strong><\/p>\n\n- \n
Python\u5177\u6709\u5f3a\u5927\u800c\u4e30\u5bcc\u7684\u673a\u5668\u5b66\u4e60\u5e93\u3002<\/strong> Python\u62e5\u6709\u4f17\u591a\u7684\u673a\u5668\u5b66\u4e60\u5e93\u548c\u6846\u67b6\uff0c\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u5404\u79cd\u7b97\u6cd5\u548c\u6a21\u578b\uff0c\u6db5\u76d6\u4e86\u673a\u5668\u5b66\u4e60\u7684\u65b9\u65b9\u9762\u9762\u3002\u5b83\u4eec\u4f7f\u5f97\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u53d8\u5f97\u66f4\u52a0\u7b80\u5355\u548c\u9ad8\u6548\u3002<\/p>\n<\/li>\n- \n
Python\u5177\u6709\u6613\u7528\u7684\u8bed\u6cd5\u548c\u4e30\u5bcc\u7684\u751f\u6001\u7cfb\u7edf\u3002<\/strong> Python\u7684\u7b80\u6d01\u6613\u8bfb\u7684\u8bed\u6cd5\u4f7f\u5f97\u5f00\u53d1\u8005\u53ef\u4ee5\u66f4\u5feb\u901f\u5730\u7f16\u5199\u673a\u5668\u5b66\u4e60\u4ee3\u7801\uff0c\u5e76\u4e14Python\u62e5\u6709\u4f17\u591a\u7684\u7b2c\u4e09\u65b9\u5e93\u548c\u5de5\u5177\uff0c\u5305\u62ec\u6570\u636e\u5904\u7406\u3001\u53ef\u89c6\u5316\u3001\u6a21\u578b\u8bc4\u4f30\u7b49\u65b9\u9762\u7684\u5e93\uff0c\u53ef\u4ee5\u63d0\u9ad8\u5f00\u53d1\u6548\u7387\u3002<\/p>\n<\/li>\n- \n
Python\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u548c\u652f\u6301\u793e\u533a\u3002<\/strong> Python\u662f\u4e00\u79cd\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u79cd\u9886\u57df\u548c\u884c\u4e1a\u7684\u7f16\u7a0b\u8bed\u8a00\uff0c\u56e0\u6b64\u6709\u8bb8\u591a\u673a\u5668\u5b66\u4e60\u5b9e\u8df5\u7ecf\u9a8c\u548c\u6848\u4f8b\u53ef\u5b66\u4e60\u3002\u6b64\u5916\uff0cPython\u62e5\u6709\u5e9e\u5927\u7684\u5f00\u6e90\u793e\u533a\uff0c\u5f00\u53d1\u8005\u53ef\u4ee5\u5728\u5176\u4e2d\u4ea4\u6d41\u7ecf\u9a8c\u3001\u5171\u4eab\u4ee3\u7801\u548c\u89e3\u51b3\u95ee\u9898\u3002<\/p>\n<\/li>\n- \n
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