{"id":36615,"date":"2021-06-21T00:01:05","date_gmt":"2021-06-20T16:01:05","guid":{"rendered":"https:\/\/case.ntu.edu.tw\/blog\/?p=36615"},"modified":"2021-06-21T10:08:32","modified_gmt":"2021-06-21T02:08:32","slug":"%e3%80%90%e6%8e%a2%e7%b4%a225-1%e3%80%91%e8%8a%9d%e9%ba%bb%e9%96%8b%e9%96%80%e2%94%80%e2%94%80%e8%97%89%e5%8a%a9%e6%a9%9f%e5%99%a8%e8%88%87%e8%aa%9e%e9%9f%b3%e9%96%8b%e5%89%b5%e4%ba%ba%e9%a1%9e","status":"publish","type":"post","link":"https:\/\/case.ntu.edu.tw\/blog\/?p=36615","title":{"rendered":"\u3010\u63a2\u7d2225-1\u3011\u829d\u9ebb\u958b\u9580\u2500\u2500\u85c9\u52a9\u6a5f\u5668\u8207\u8a9e\u97f3\u958b\u5275\u4eba\u985e\u6587\u660e"},"content":{"rendered":"<p><iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/NPypJSYIlGo\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p><span style=\"color: #333399;\"><strong>\u8a9e\u8a00\u662f\u4eba\u985e\u6587\u660e\u9032\u5c55\u7684\u5a92\u4ecb\uff0c\u4e26\u85c9\u7531\u6587\u5b57\u6216\u8072\u97f3\u7b49\u5f62\u5f0f\u50b3\u905e\uff0c\u96d6\u7136\u6a5f\u5668\u5728\u8fa8\u8b58\u751a\u81f3\u7de8\u5beb\u6587\u5b57\u8a0a\u606f\u5df2\u6709\u76f8\u7576\u8c50\u5bcc\u7684\u6210\u679c\uff0c\u4f46\u8a9e\u97f3\u4e16\u754c\u7684\u5927\u9580\u624d\u525b\u88ab\u958b\u555f\u3002\u5728\u73fe\u4eca\u4eba\u624b\u4e00\u6a5f\u7684\u6642\u4ee3\uff0c\u9664\u8a9e\u97f3\u8fa8\u8b58\u548c\u8f38\u5165\u63d0\u9ad8\u8cc7\u8a0a\u50b3\u905e\u7684\u6548\u7387\u5916\uff0cSiri\u3001Google \u7b49\u8a9e\u97f3\u52a9\u7406\u7684\u51fa\u73fe\uff0c\u5728\u4fbf\u5229\u4e4b\u5916\u4e5f\u589e\u6dfb\u4e86\u751f\u6d3b\u7684\u6a02\u8da3\u3002\u4f46\u7a76\u7adf\u9019\u4e9b\u667a\u80fd\u8a9e\u97f3\u52a9\u7406\uff0c\u662f\u5982\u4f55\u80fd\u5920\u807d\u5f97\u61c2\u4eba\u985e\u8a9e\u8a00\uff1f\u53c8\u5982\u4f55\u96a8\u8457\u4e00\u4ee3\u4ee3\u7a0b\u5f0f\u7684\u66f4\u65b0\u4e0d\u65b7\u300c\u9032\u5316\u300d\u7684\u5462\uff1f\u7531\u4e2d\u6587\u8a9e\u97f3\u8fa8\u8b58\u7684\u5148\u9a45\u3001\u53f0\u5927\u96fb\u6a5f\u7cfb\u7684\u7279\u8058\u6559\u6388\u674e\u7433\u5c71\uff0c\u63ed\u958b\u6a5f\u5668\u7406\u89e3\u4eba\u985e\u8a9e\u8a00\u7684\u795e\u79d8\u9762\u7d17\uff0c\u4e26\u5e36\u9818\u5927\u5bb6\u4e00\u540c\u63a2\u7a76\u6a5f\u5668\u7684\u9032\u5316\u5c0d\u4eba\u985e\u767c\u5c55\u7684\u5f71\u97ff\u3002<\/strong><\/span><\/p>\n<p><strong>\u8b1b\u8005\uff0f\u4e2d\u592e\u7814\u7a76\u9662\u9662\u58eb\/\u81fa\u7063\u5927\u5b78\u96fb\u6a5f\u5de5\u7a0b\u5b78\u7cfb\u7279\u8058\u6559\u6388 \u674e\u7433\u5c71<br \/>\n<\/strong><strong>\u5f59\u6574\u64b0\u6587\uff0f\u5442\u65b9\u96ef<\/strong><\/p>\n<p><span style=\"color: #008080;\"><strong>\u25cf\u6a5f\u5668\u5982\u4f55\u807d\u61c2\u4eba\u985e\u8a9e\u8a00<\/strong><\/span><\/p>\n<p>\u8a9e\u8a00\u662f\u4eba\u985e\u8207\u52d5\u7269\u6700\u4e3b\u8981\u7684\u5dee\u5225\u4e4b\u4e00\uff0c\u4e5f\u662f\u5b55\u80b2\u4eba\u985e\u6587\u660e\u7684\u91cd\u8981\u5143\u7d20\u3002\u5728\u8a9e\u97f3\u7684\u90e8\u5206\uff0c\u5507\u9f52\u5728\u53e3\u8154\u4e2d\u7684\u5404\u7a2e\u7cbe\u7d30\u904b\u4f5c\uff0c\u7d44\u6210\u4e86\u8c50\u5bcc\u7684\u57fa\u672c\u97f3\u8207\u5b57\u8a5e\uff0c\u800c\u5de5\u7a0b\u5e2b\u70ba\u4e86\u8981\u8b93\u6a5f\u5668\u9664\u4e86\u8a18\u61b6\u8cc7\u6599\u3001\u91cd\u8907\u57f7\u884c\u4efb\u52d9\u5916\uff0c\u9084\u80fd\u7406\u89e3\u8fa8\u8b58\u4eba\u985e\u7684\u8a9e\u8a00\u3001\u5c55\u73fe\u51fa\u770b\u4f3c\u6d3b\u5b78\u6d3b\u7528\u7684\u300c\u4eba\u5de5\u667a\u6167\u300d\uff0c\u5176\u4e2d\u4e00\u500b\u65b9\u5f0f\u4fbf\u662f\u985e\u795e\u7d93\u7db2\u8def (neural network) \u6a21\u578b\u3002<\/p>\n<p>\u985e\u795e\u7d93\u7db2\u8def\u7684\u6982\u5ff5\u4f86\u81ea\u8166\u795e\u7d93\u79d1\u5b78\uff0c\u795e\u7d93\u5143\u5f7c\u6b64\u9023\u7d50\u3001\u50b3\u905e\u8a0a\u606f\uff0c\u800c\u5728\u985e\u795e\u7d93\u7db2\u8def\u4e2d\uff0c\u6bcf\u500b\u7bc0\u9ede\u90fd\u548c\u4e0a\u4e0b\u5c64\u7684\u5176\u4ed6\u7bc0\u9ede\u5f7c\u6b64\u76f8\u9023\u3002\u5728\u8f38\u5165\u3001\u8f38\u51fa\u5c64\u4e4b\u9593\uff0c\u5247\u6709\u8457\u5927\u91cf\u7684\u96b1\u85cf\u5c64 (hidden layers)\uff0c\u4e00\u5c64\u4e00\u5c64\u5730\u64f7\u53d6\u8f38\u5165\u8cc7\u6599\u7684\u7279\u5fb5\u3002\u5728\u8907\u96dc\u7684\u795e\u7d93\u7db2\u8def\u6a21\u578b\u4e2d\uff0c\u56e0\u70ba\u6709\u5de8\u91cf\u53ef\u8abf\u6574\u7684\u53c3\u6578\u548c\u8cc7\u6599\uff0c\u80fd\u5927\u5e45\u63d0\u5347\u7cbe\u7d30\u5ea6\uff0c\u96d6\u7136\u672c\u8cea\u4e0a\u4ecd\u662f\u900f\u904e\u4e00\u5f35\u8d85\u7cbe\u7d30\u7684\u8ab2\u8868\u4f86\u57f7\u884c\u4efb\u52d9\uff0c\u4f46\u770b\u8d77\u4f86\u50cf\u662f\u300c\u6d3b\u5b78\u6d3b\u7528\u300d\u3002\u8209\u4f8b\u4f86\u8aaa\uff0c\u7576\u6a5f\u5668\u5b78\u7fd2\u4e86\u4e0a\u842c\u5f35\u4e0d\u540c\u8c93\u54aa\u7684\u7167\u7247\u3001\u64f7\u53d6\u51fa\u5c6c\u65bc\u8c93\u54aa\u7684\u5171\u540c\u7279\u5fb5\u5f8c\uff0c\u672a\u4f86\u96d6\u7136\u770b\u5230\u4e00\u5f35\u5f9e\u672a\u770b\u904e\u7684\u8c93\u54aa\u5716\u50cf\uff0c\u537b\u4ecd\u7136\u6709\u6a5f\u6703\u6b63\u78ba\u505a\u51fa\u5224\u65b7\u3002<\/p>\n<p>\u61c9\u7528\u5230\u9019\u6b21\u7684\u4e3b\u89d2\u300c\u8a9e\u97f3\u300d\u4e0a\uff0c\u8072\u97f3\u7684\u97f3\u6ce2\u80fd\u5920\u8f15\u6613\u8f49\u63db\u6210\u96fb\u8a0a\u865f\uff0c\u4e26\u900f\u904e\u5bcc\u6c0f\u8f49\u63db (fourier transformation)&nbsp; \u5e6b\u52a9\u5f8c\u7e8c\u5206\u6790\u9032\u884c\u3002\u800c\u7279\u5fb5\u64f7\u53d6 (feature extraction) \u7528\u5728\u8a9e\u97f3\u8fa8\u8b58\u4e0a\uff0c\u5247\u53ef\u4ee5\u64f7\u53d6\u51fa\u6bcf\u6bb5\u97f3\u983b\u8207\u5507\u9f52\u820c\u76f8\u5c0d\u95dc\u4fc2\u6709\u95dc\u7684\u53c3\u6578\uff0c\u662f\u4e00\u500b\u6578\u5341\u7dad\u7684\u5de8\u5927\u5411\u91cf\uff0c\u4ee5\u4fbf\u9032\u884c\u5f8c\u7e8c\u5206\u6790\u3001\u6bd4\u5c0d\u4e26\u9078\u64c7\u51fa\u9069\u5408\u7684\u6a21\u578b\u3002\u5728\u767c\u5c55\u548c\u9078\u64c7\u6a21\u578b\u6642\uff0c\u5247\u5e38\u6703\u7528\u5230\u6578\u5b78\u4e2d\u7684\u96b1\u85cf\u5f0f\u99ac\u53ef\u592b\u6a21\u578b (hidden Markov models, HMM) \u548c\u72c0\u614b\u8f49\u79fb\u77e9\u9663 (state transition porbabilities)\uff0c\u900f\u904e\u8f38\u5165\u8cc7\u6599\u7684\u5b78\u7fd2\uff0c\u8a08\u7b97\u51fa\u4e0d\u540c\u8a9e\u97f3\u7d44\u5408\u6703\u51fa\u73fe\u7684\u6a5f\u7387\u53ca\u5176\u5c0d\u61c9\u7684\u6587\u5b57\u578b\u614b\uff0c\u9032\u800c\u505a\u51fa\u6709\u6548\u7684\u5224\u65b7\u8207\u56de\u61c9\u3002<\/p>\n<p><span style=\"color: #008080;\"><strong>\u25cf\u52a0\u901f\u8a9e\u97f3\u8fa8\u8b58\u7684\u6df1\u5c64\u5b78\u7fd2<\/strong><strong>&nbsp;<\/strong><\/span><\/p>\n<p>\u6709\u8da3\u7684\u662f\uff0c\u8a31\u591a\u6642\u5019\u6a5f\u5668\u7684\u300c\u6df1\u5ea6\u5b78\u7fd2\u300d\u4e0d\u53ea\u4f86\u81ea\u4e00\u53f0\u6a5f\u5668\uff0c\u800c\u662f\u80fd\u900f\u904e\u5c0d\u6297\u5f0f\u5b78\u7fd2 (adversial learning) \u8207\u751f\u6210\u5c0d\u6297\u7db2\u8def (generative adversarial network, GAN) \u8b93\u6a5f\u5668\u6709\u6a5f\u6703\u5c55\u73fe\u51fa\u300c\u5275\u4f5c\u300d\u3002\u4ee5\u5169\u53f0\u6a5f\u5668\u70ba\u4f8b\uff0c\u5176\u4e2d\u4e00\u53f0\u7a31\u70ba\u751f\u6210\u5668 (generator)\uff0c\u4e0d\u65b7\u751f\u7522\u5275\u4f5c\uff0c\u800c\u53e6\u4e00\u53f0\u9451\u5225\u5668(discriminator) \u5247\u662f\u627e\u51fa\u751f\u6210\u5668\u7684\u7d50\u679c\u8207\u76ee\u6a19\u7d50\u679c\u9593\u7684\u5dee\u7570\u4e26\u8a55\u5206\u3002\u9019\u6a23\u53cd\u8986\u56fa\u5b9a\u4e00\u53f0\u6a5f\u5668\u8a13\u7df4\u53e6\u4e00\u53f0\uff0c\u5728\u4e92\u76f8\u5c0d\u6297\u4e2d\u5f7c\u6b64\u5b78\u7fd2\u5f8c\uff0c\u751f\u6210\u5668\u7684\u7522\u51fa\u6210\u54c1\u5c31\u6703\u8ddf\u9451\u5225\u5668\u4e2d\u7684\u4eba\u985e\u4f5c\u54c1\u8d8a\u4f86\u8d8a\u50cf\u537b\u4e0d\u76e1\u76f8\u540c\uff0c\u4e5f\u8b93\u5be6\u8cea\u4e0d\u65b7\u91cd\u8907\u4efb\u52d9\u7684\u6a5f\u5668\uff0c\u505a\u51fa\u770b\u4f3c\u300c\u6709\u5275\u610f\u300d\u7684\u4f5c\u54c1\u3002<\/p>\n<p>\u904e\u53bb\u4eba\u985e\u900f\u904e\u300c\u8a9e\u8a00\u5b78\u300d\u9019\u5957\u77e5\u8b58\u7cfb\u7d71\u4f86\u7406\u89e3\u8a9e\u8a00\u7684\u67b6\u69cb\uff0c\u800c\u5982\u679c\u63a1\u7528\u7aef\u5230\u7aef\u6df1\u5c64\u5b78\u7fd2 (end-to-end deep learning) 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