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\u672c\u4e66\u5728\u7b2c 1 \u7ae0\u548c\u7b2c 2 \u7ae0\u4ecb\u7ecd\u4e86\u6df1\u5ea6\u5b66\u4e60\u7684\u57fa\u7840\uff1a\u6570\u5b66\u57fa\u7840\u4e0e\u795e\u7ecf\u7f51\u7edc\u7b97\u6cd5\u3002\u4ece\u7b2c 3 \u7ae0\u5f00\u59cb\uff0c\u4e66\u7c4d\u9010\u6b65\u5f15\u9886\u8bfb\u8005\u8fdb\u5165\u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u9886\u57df\uff0c\u5373\u4e00\u4e9b\u57fa\u4e8e\u795e\u7ecf\u7f51\u7edc\u7684\u53d8\u4f53\u7b97\u6cd5\uff1a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u3001\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u3001\u7f16\u7801\u5668-\u89e3\u7801\u5668\u6a21\u578b\uff0c\u4ee5\u53ca\u76ee\u524d\u706b\u70ed\u7684\u53d8\u5f62\u91d1\u521a\u7b97\u6cd5\u3001\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u548c\u6269\u6563\u6a21\u578b\u3002\u8fd9\u4e9b\u7ae0\u8282\u4e0d\u4ec5\u8bb2\u89e3\u4e86\u5404\u4e2a\u6a21\u578b\u7684\u57fa\u7840\u7406\u8bba\u548c\u5173\u952e\u6280\u672f\uff0c\u8fd8\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u8fd9\u4e9b\u6a21\u578b\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u7b49\u9886\u57df\u7684\u5e94\u7528\u6848\u4f8b\u3002\u4e66\u7c4d\u7684\u540e\u534a\u90e8\u5206\u805a\u7126\u4e8e\u56fe\u795e\u7ecf\u7f51\u7edc\u548c\u5f3a\u5316\u5b66\u4e60\u8fd9\u4e9b\u524d\u6cbf\u7b97\u6cd5\uff0c\u6df1\u5165\u6d45\u51fa\u5730\u8bb2\u89e3\u4e86\u5b83\u4eec\u7684\u57fa\u7840\u77e5\u8bc6\u3001\u7b97\u6cd5\u53d8\u4f53\u53ca\u7ecf\u5178\u6a21\u578b\u7b49\u9ad8\u7ea7\u8bdd\u9898\u3002
\u8fd9\u4e9b\u5185\u5bb9\u4e3a\u8bfb\u8005\u7406\u89e3\u548c\u5e94\u7528\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u63d0\u4f9b\u4e86\u575a\u5b9e\u7684\u7406\u8bba\u57fa\u7840\u3002<\/p>\n\n\n\n
\u672c\u4e66\u9002\u5408\u5bf9\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u611f\u5174\u8da3\u7684\u672c\u79d1\u751f\u3001\u7814\u7a76\u751f\u53ca\u76f8\u5173\u884c\u4e1a\u7684\u4ece\u4e1a\u8005\u9605\u8bfb\u3002\u672c\u4e66\u65e8\u5728\u5e2e\u52a9\u8bfb\u8005\u638c\u63e1\u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u6280\u672f\uff0c\u6fc0\u53d1\u521b\u65b0\u601d\u7ef4\uff0c\u63a8\u52a8\u4e2a\u4eba\u548c\u884c\u4e1a\u7684\u53d1\u5c55\u3002<\/p>\n\n\n\n
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\u300a\u5168\u89e3\u6df1\u5ea6\u5b66\u4e60\u4e5d\u5927\u7b97\u6cd5\u300b\u662f\u4e3a\u4e86\u90a3\u4e9b\u52c7\u6562\u7684\u63a2\u7d22\u8005\u800c\u5199\u7684\u3002\u6211\u4eec\u7684\u65c5\u7a0b\u4ece\u6df1\u5ea6\u5b66\u4e60\u7684\u57fa\u7840\u6570\u5b66\u539f\u7406\u51fa\u53d1\uff0c\u50cf\u662f\u5728\u832b\u832b\u5927\u6d77\u4e2d\u8bbe\u7f6e\u7684\u706f\u5854\uff0c\u4e3a\u822a\u884c\u8005\u4eec\u6307\u5f15\u65b9\u5411\uff0c\u7136\u540e\u6211\u4eec\u4e00\u8d77\u6df1\u5165\u63a2\u8ba8\u795e\u7ecf\u7f51\u7edc\u7684\u5965\u79d8\uff0c\u63ed\u5f00\u5377\u79ef\u3001\u5faa\u73af\u53ca\u5176\u4ed6\u590d\u6742\u6a21\u578b\u80cc\u540e\u7684\u9762\u7eb1\uff0c\u8ba9\u8fd9\u4e9b\u77e5\u8bc6\u4e0d\u518d\u9065\u4e0d
\u53ef\u53ca\u3002<\/p>\n\n\n\n
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\u672c\u4e66\u4e3b\u8981\u5185\u5bb9
\u7b2c 1 \u7ae0\uff1a\u6df1\u5ea6\u5b66\u4e60\u7684\u6570\u5b66\u57fa\u7840\uff0c\u5305\u62ec\u5fae\u79ef\u5206\u3001\u7ebf\u6027\u4ee3\u6570\u3001\u6982\u7387\u8bba\u548c\u7edf\u8ba1\u5b66\uff0c\u4e3a\u8bfb\u8005\u540e\u7eed\u7684\u5b66\u4e60\u5960\u5b9a\u575a\u5b9e\u7684\u57fa\u7840\u3002<\/p>\n\n\n\n
\u7b2c 2 \u7ae0\uff1a\u4ecb\u7ecd\u4e86\u795e\u7ecf\u7f51\u7edc\u7684\u7406\u8bba\u57fa\u7840\uff0c\u5305\u62ec\u7ebf\u6027\u6a21\u578b\u3001\u635f\u5931\u51fd\u6570\u3001\u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5\u7b49\uff0c\u4e3a\u7406\u89e3\u66f4\u590d\u6742\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u6253\u4e0b\u57fa\u7840\u3002<\/p>\n\n\n\n
\u7b2c 3 \u7ae0\uff1a\u805a\u7126\u4e8e\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\uff0c\u4ece\u5176\u8ba1\u7b97\u65b9\u6cd5\u5230\u7279\u5f81\u63d0\u53d6\u8fc7\u7a0b\uff0c\u8be6\u7ec6\u4ecb\u7ecd\u4e86CNN \u5728\u56fe\u50cf\u8bc6\u522b\u4e2d\u7684\u5173\u952e\u4f5c\u7528\u548c\u5e94\u7528\u5b9e\u4f8b\u3002<\/p>\n\n\n\n
\u7b2c 4 \u7ae0\uff1a\u6df1\u5165\u8bb2\u89e3\u4e86\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\uff08RNN\uff09\u53ca\u5176\u53d8\u4f53\u6a21\u578b\uff0c\u5982 LSTM \u548c GRU\uff0c\u5c55\u793a\u4e86\u5b83\u4eec\u5728\u5904\u7406\u5e8f\u5217\u6570\u636e\uff0c\u7279\u522b\u662f\u8bed\u8a00\u6a21\u578b\u548c\u6587\u672c\u9884\u5904\u7406\u65b9\u9762\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n
\u7b2c 5 \u7ae0\uff1a\u63a2\u8ba8\u4e86\u7f16\u7801\u5668-\u89e3\u7801\u5668\u6a21\u578b\uff0c\u5305\u62ec\u5176\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684\u6838\u5fc3\u5e94\u7528\uff0c\u5982 Seq2Seq\u3001VAE \u6a21\u578b\u7b49\u3002<\/p>\n\n\n\n
\u7b2c 6 \u7ae0\uff1a\u8be6\u8ff0\u4e86\u53d8\u5f62\u91d1\u521a\u7b97\u6cd5\u7684\u57fa\u7840\u77e5\u8bc6\u548c\u5e94\u7528\uff0c\u7279\u522b\u662f\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684 Transformer \u6a21\u578b\uff0c\u5982 BERT \u548c Vision Transformer\uff0c\u4ee5\u53ca\u5b83\u4eec\u5982\u4f55\u6539\u53d8\u4e86\u4f20\u7edf\u6a21\u578b\u7684\u4f7f\u7528\u548c\u6548\u679c\u3002<\/p>\n\n\n\n
\u7b2c 7 \u7ae0\uff1a\u6df1\u5165\u5206\u6790\u4e86\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff08GAN\uff09\u53ca\u5176\u6539\u8fdb\u6a21\u578b\uff0c\u63a2\u8ba8\u4e86 GAN \u5728\u56fe\u50cf\u751f\u6210\u3001\u6a21\u5f0f\u5d29\u6e83\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6cd5\u7b49\u65b9\u9762\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n
\u7b2c 8 \u7ae0\uff1a\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u6269\u6563\u6a21\u578b\uff0c\u7279\u522b\u662f Denoising Diffusion Probabilistic Models\uff08DDPM\uff09\u7684\u539f\u7406\u548c\u5e94\u7528\uff0c\u5c55\u793a\u4e86\u8fd9\u4e00\u7c7b\u6a21\u578b\u5728\u751f\u6210\u4efb\u52a1\u4e2d\u7684\u6f5c\u529b\u3002<\/p>\n\n\n\n
\u7b2c 9 \u7ae0\uff1a\u63a2\u8ba8\u4e86\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u57fa\u7840\u548c\u6a21\u578b\uff0c\u5305\u62ec GCN\u3001GraphSAGE\u3001Graph Attention Network \u7b49\uff0c\u4ee5\u53ca\u5b83\u4eec\u5728\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u4e2d\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n
\u7b2c 10 \u7ae0\uff1a\u8bb2\u8ff0\u4e86\u5f3a\u5316\u5b66\u4e60\u7684\u57fa\u672c\u6982\u5ff5\u3001\u57fa\u4e8e\u4ef7\u503c\u548c\u57fa\u4e8e\u7b56\u7565\u7684\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u65b9\u6cd5\uff0c\u4ee5\u53ca\u6f14\u5458-\u8bc4\u8bba\u5bb6\u6a21\u578b\uff0c\u5c55\u793a\u4e86\u5f3a\u5316\u5b66\u4e60\u5728\u51b3\u7b56\u8fc7\u7a0b\u4e2d\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n
\u672c\u4e66\u901a\u8fc7\u8fd9\u5341\u7ae0\u7684\u5185\u5bb9\uff0c\u4e3a\u8bfb\u8005\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6df1\u5ea6\u5b66\u4e60\u9886\u57df\u4ece\u5165\u95e8\u5230\u8fdb\u9636\u7684\u5168\u9762\u6307\u5357\uff0c\u65e8\u5728\u5e2e\u52a9\u8bfb\u8005\u7406\u89e3\u6df1\u5ea6\u5b66\u4e60\u7684\u6838\u5fc3\u7406\u8bba\u3001\u638c\u63e1\u4e3b\u8981\u6280\u672f\uff0c\u5e76\u5e94\u7528\u4e8e\u5b9e\u8df5\u4e2d\uff0c\u63a8\u52a8\u4e2a\u4eba\u548c\u884c\u4e1a\u7684\u53d1\u5c55\u3002<\/p>\n\n\n\n
\u7b2c\u4e00\u7ae0 \u6df1\u5ea6\u5b66\u4e60\u6570\u5b66\u57fa\u7840
1.1\u9ad8\u7b49\u6570\u5b66\u4e4b\u5fae\u79ef\u5206
1.1.1\u91cd\u8bc6\u5fae\u5206
1.1.2\u5fae\u5206\u7684\u89e3\u8bfb
1.1.3\u5fae\u5206\u4e0e\u51fd\u6570\u7684\u5355\u8c03\u6027\u548c\u51f9\u51f8\u6027
1.1.4\u5fae\u5206\u7684\u94fe\u5f0f\u6cd5\u5219
1.1.5\u504f\u5fae\u5206\u4e0e\u5168\u5fae\u5206
1.1.6\u68af\u5ea6\u4e0e\u65b9\u5411\u5bfc\u6570
1.1.7\u6cf0\u52d2\u516c\u5f0f\u4e0e\u9ea6\u514b\u52b3\u6797\u516c\u5f0f
1.1.8\u62c9\u683c\u6717\u65e5\u4e58\u5b50\u6cd5
1.1.9\u91cd\u8bc6\u79ef\u5206
1.1.10\u4e0d\u5b9a\u79ef\u5206\u548c\u53cd\u5bfc\u6570
1.1.11\u5b9a\u79ef\u5206\u4e0e\u725b\u987f-\u83b1\u5e03\u5c3c\u8328\u516c\u5f0f
1.1.12\u5fae\u79ef\u5206\u7684\u57fa\u672c\u5b9a\u7406
1.2\u7ebf\u6027\u4ee3\u6570
1.2.1\u7ebf\u6027\u65b9\u7a0b\u7ec4
1.2.2 \u7ebf\u6027\u65b9\u7a0b\u7ec4\u7684\u77e9\u9635\u6c42\u89e3\u6cd5
1.2.3 \u77e9\u9635\u4e58\u6cd5
1.2.4 \u5411\u91cf\u7684\u6570\u4e58
1.2.5 \u5411\u91cf\u7684\u52a0\u6cd5
1.2.6\u5411\u91cf\u7684\u7ebf\u6027\u7ec4\u5408
1.2.7 \u5411\u91cf\u7a7a\u95f4
1.2.8\u5411\u91cf\u7684\u7ebf\u6027\u76f8\u5173\u548c\u7ebf\u6027\u65e0\u5173
1.2.9 \u5411\u91cf\u4e58\u6cd5
1.2.10 \u5411\u91cf\u7684\u6b63\u4ea4
1.2.11 \u5411\u91cf\u4e0e\u77e9\u9635
1.2.12 \u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf
1.3\u6982\u7387\u8bba
1.3.1 \u9891\u6570
1.3.2 \u6570\u636e\u4f4d\u7f6e
1.3.3 \u6570\u636e\u6563\u5e03
1.3.4 \u968f\u673a\u53d8\u91cf\u7684\u7c7b\u578b\u548c\u6982\u7387\u5206\u5e03
1.3.5 \u7406\u8bba\u6982\u7387\u5206\u5e03\u4e4b\u5e38\u89c1\u7684\u79bb\u6563\u578b\u5206\u5e03
1.3.6 \u7406\u8bba\u6982\u7387\u5206\u5e03\u4e4b\u5e38\u89c1\u7684\u8fde\u7eed\u578b\u5206\u5e03
1.3.7 \u7ecf\u9a8c\u6982\u7387\u5206\u5e03
1.4\u7edf\u8ba1\u5b66
1.4.1\u5927\u6570\u5b9a\u5f8b\u4e0e\u4e2d\u5fc3\u6781\u9650\u5b9a\u7406
1.4.2\u53c2\u6570\u4f30\u8ba1
1.4.3\u7edf\u8ba1\u91cf\u548c\u62bd\u6837\u5206\u5e03
1.4.4\u5047\u8bbe\u68c0\u9a8c
1.4.5\u76f8\u5173\u6027\u5206\u6790
\u7b2c\u4e8c\u7ae0 \u795e\u7ecf\u7f51\u7edc\u7406\u8bba\u57fa\u7840
2.1 \u7ebf\u6027\u6a21\u578b
2.1.1 \u7ebf\u6027\u6a21\u578b\u7684\u5b9a\u4e49
2.1.2 \u635f\u5931\u51fd\u6570
2.1.3 \u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5
2.1.4 \u5e7f\u4e49\u7ebf\u6027\u6a21\u578b
2.2 \u56de\u5f52\u4e0e\u5206\u7c7b
2.2.1 \u56de\u5f52\u548c\u5206\u7c7b\u95ee\u9898\u7684\u5b9a\u4e49\u4e0e\u8054\u7cfb
2.2.2 \u7ebf\u6027\u6a21\u578b\u89e3\u51b3\u56de\u5f52\u548c\u5206\u7c7b\u95ee\u9898
2.3 \u611f\u77e5\u673a\u6a21\u578b
2.3.1\u611f\u77e5\u673a\u6a21\u578b\u5b9a\u4e49\u4e0e\u7406\u89e3
2.3.2 \u795e\u7ecf\u7f51\u7edc\u7b97\u6cd5\u4e0e\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b
2.3.3 \u53cd\u5411\u4f20\u64ad\u7b97\u6cd5
2.4\u6fc0\u6d3b\u51fd\u6570
2.4.1\u6fc0\u6d3b\u51fd\u6570\u7684\u5b9a\u4e49\u4e0e\u4f5c\u7528
2.4.2\u5e38\u7528\u6fc0\u6d3b\u51fd\u6570
2.5\u7ef4\u5ea6\u8bc5\u5492
2.5.1\u795e\u7ecf\u7f51\u7edc\u7684\u5c42\u7ea7\u7ed3\u6784
2.5.2\u7ef4\u5ea6\u8bc5\u5492\u4e0e\u8fc7\u62df\u5408
2.6\u8fc7\u62df\u5408\u4e0e\u6b20\u62df\u5408
2.6.1\u8fc7\u62df\u5408\u548c\u6b20\u62df\u5408\u73b0\u8c61\u7684\u5b9a\u4e49
2.6.2\u8fc7\u62df\u5408\u548c\u6b20\u62df\u5408\u73b0\u8c61\u7684\u4ea7\u751f\u539f\u56e0
2.7\u6b63\u5219
2.7.1 L1\u548cL2\u6b63\u5219
2.7.2 Dropout
2.8\u6570\u636e\u589e\u5f3a
2.9\u6570\u503c\u4e0d\u7a33\u5b9a\u6027
\u7b2c\u4e09\u7ae0 \u5377\u79ef\u795e\u7ecf\u7f51\u7edc
3.1\u5377\u79ef\u795e\u7ecf\u7f51\u7edc
3.1.1 \u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u8ba1\u7b97
3.1.2\u5377\u79ef\u7684\u8bbe\u8ba1\u601d\u60f3
3.1.3\u5377\u79ef\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\u7684\u8fc7\u7a0b
3.1.4\u6c60\u5316\u4e0e\u91c7\u6837
3.1.5\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u611f\u53d7\u91ce
3.1.6\u5377\u79ef\u6a21\u578b\u5b9e\u73b0\u56fe\u50cf\u8bc6\u522b
3.1.7\u7b2c\u4e00\u4e2a\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff1aLeNet
3.2\u5377\u79ef\u7684\u53d8\u4f53\u7b97\u6cd5
3.2.1 \u9010\u901a\u9053\u5377\u79ef
3.2.2 \u9010\u70b9\u5377\u79ef
3.2.3 \u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef
3.2.4 \u7ec4\u5377\u79ef
3.2.5 \u7a7a\u95f4\u53ef\u5206\u79bb\u5377\u79ef
3.2.6 \u7a7a\u6d1e\u5377\u79ef
3.2.7 \u8f6c\u7f6e\u5377\u79ef
3.2.8 \u7a00\u758f\u5377\u79ef
3.2.9 \u591a\u7ef4\u5377\u79ef\uff08*D Convolution\uff09
\u7b2c\u56db\u7ae0 \u5faa\u73af\u795e\u7ecf\u7f51\u7edc
4.1\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u57fa\u7840
4.1.1\u5e8f\u5217\u6570\u636e
4.1.2 RNN\u6a21\u578b
4.1.3\u8bed\u8a00\u6a21\u578b
4.1.4 \u6587\u672c\u9884\u5904\u7406
4.1.5 \u5efa\u6a21\u548c\u9884\u6d4b
4.2\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u7684\u53d8\u4f53\u6a21\u578b
4.2.1\u95e8\u63a7\u5faa\u73af\u5355\u5143
4.2.2\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc
4.2.3\u6df1\u5ea6\u5faa\u73af\u795e\u7ecf\u7f51\u7edc
4.2.4 \u53cc\u5411\u5faa\u73af\u795e\u7ecf\u7f51\u7edc
4.2.5\u57fa\u4e8e\u6ce8\u610f\u529b\u7684\u5faa\u73af\u795e\u7ecf\u7f51\u7edc
\u7b2c\u4e94\u7ae0 \u7f16\u7801\u5668-\u89e3\u7801\u5668\u6a21\u578b
5.1\u7f16\u7801\u5668-\u89e3\u7801\u5668\u6a21\u578b
5.1.1 Encoder-Decoder\u6a21\u578b\u7684\u57fa\u672c\u7ed3\u6784
5.1.2 Encoder-Decoder\u6a21\u578b\u5728\u81ea\u7136\u8bed\u97f3\u5904\u7406\u9886\u57df\u7684\u5e94\u7528
5.1.3 Encoder-Decoder\u6a21\u578b\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684\u5e94\u7528
5.1.4 \u81ea\u7f16\u7801\u5668\u6a21\u578b\uff1aAuto-Encoder
5.2 CV\u4e2d\u7684\u7f16\u7801\u5668-\u89e3\u7801\u5668\uff1aVAE\u6a21\u578b
5.2.1 VAE\u6a21\u578b\u7b80\u660e\u6307\u5bfc
5.2.2 \u6f5c\u7a7a\u95f4
5.2.3 \u6781\u5927\u4f3c\u7136\u4f30\u8ba1
5.2.4 \u9690\u53d8\u91cf\u6a21\u578b
5.2.5 \u8499\u7279\u5361\u6d1b\u91c7\u6837
5.2.6 \u53d8\u5206\u63a8\u65ad
5.3 NLP\u4e2d\u7684\u7f16\u7801\u5668-\u89e3\u7801\u5668\uff1aSeq2Seq\u6a21\u578b
5.3.1 Seq2Seq\u7f16\u7801\u5668
5.3.2 Seq2Seq\u89e3\u7801\u5668
5.3.3 Seq2Seq\u7684attention\u673a\u5236
5.3.4 Seq2Seq\u7684teacher forcing\u7b56\u7565
5.3.5 Seq2Seq\u8bc4\u4ef7\u6307\u6807BLEU
5.3.6 Seq2Seq\u6a21\u578b\u5c0f\u7ed3
\u7b2c\u516d\u7ae0\uff1a\u53d8\u5f62\u91d1\u521a\u7b97\u6cd5
6.1 \u7b97\u6cd5\u57fa\u7840
6.1.1 \u7b97\u6cd5\u6982\u51b5
6.1.2 \u81ea\u6ce8\u610f\u529b\u5c42
6.1.3 \u591a\u5934\u81ea\u6ce8\u610f\u529b\u5c42
6.1.4 \u7f16\u7801\u5668\u7ed3\u6784
6.1.5 \u89e3\u7801\u5668\u7ed3\u6784
6.1.6 \u7ebf\u6027\u9876\u5c42\u548c Softmax \u5c42
6.1.7 \u8f93\u5165\u6570\u636e\u7684\u5411\u91cf\u5316
6.2 NLP\u4e2d\u7684Transformer\u6a21\u578b
6.2.1 Bert
6.2.2 GPT
6.3 CV\u4e2d\u7684Transformer\u6a21\u578b
6.3.1 Vision Transformer
6.3.2 Swin Transformer
6.4 Transformer\u5c0f\u7ed3
\u7b2c\u4e03\u7ae0 \u751f\u6210\u5bf9\u6297\u7f51\u7edc
7.1 Generative Adversarial Network
7.1.1 GAN\u7684\u6a21\u578b\u7ed3\u6784
7.1.2 GAN\u6a21\u578b\u7684\u8bad\u7ec3
7.2 Improved GANs
7.2.1\u6a21\u5f0f\u5d29\u6e83\uff08mode collapse\uff09
7.2.2\u6a21\u5f0f\u5d29\u6e83\u7684\u89e3\u51b3\u65b9\u6cd5
7.3 f-GAN
7.3.1 GAN\u6a21\u578b\u635f\u5931\u4e0e\u6563\u5ea6
7.3.2 GAN\u635f\u5931\u7684\u901a\u7528\u6846\u67b6f-\u6563\u5ea6
7.4 WGAN
7.4.1\u4f20\u7edf\u7684GAN\u6a21\u578b\u68af\u5ea6\u6d88\u5931\u7684\u5206\u6790
7.4.2 Wasserstein\u8ddd\u79bb
7.4.3 \u7531Wasserstein\u8ddd\u79bb\u63a8\u5bfcWGAN\u7684\u635f\u5931
7.4.4 \u4f7f\u7528\u68af\u5ea6\u60e9\u7f5a
7.5 CycleGAN
7.5.1 \u5faa\u73af\u4e00\u81f4\u6027
7.5.2 \u5bf9\u6297\u8bad\u7ec3
7.5.3 \u635f\u5931\u51fd\u6570
7.5.4 \u8bad\u7ec3\u6d41\u7a0b
7.5.5 \u5c0f\u7ed3
\u7b2c\u516b\u7ae0\uff1a\u6269\u6563\u6a21\u578b
8.1 \u6269\u6563\u6a21\u578b\u57fa\u7840
8.1.1\u6269\u6563\u6a21\u578b\u7684\u57fa\u672c\u539f\u7406
8.1.2 DDPM\u6269\u6563\u6a21\u578b\u4e0e\u53d8\u5206\u81ea\u7f16\u7801\u5668\u7684\u6bd4\u8f83
8.2 Denoising Diffusion Probabilistic Models \uff08DDPM\uff09
8.2.1 DDPM\u524d\u5411\u6269\u6563\u7b80\u660e\u6307\u5bfc
8.2.2 DDPM\u53cd\u5411\u53bb\u566a\u8fc7\u7a0b
8.2.3 DDPM\u6269\u6563\u6a21\u578b\u7684\u635f\u5931\u51fd\u6570
8.2.4 DDPM\u6269\u6563\u6a21\u578b\u7684\u4f7f\u7528
\u7b2c\u4e5d\u7ae0\uff1a\u56fe\u795e\u7ecf\u7f51\u7edc
9.1 \u56fe\u795e\u7ecf\u7f51\u7edc\u7b97\u6cd5\u57fa\u7840
9.1.1\u56fe\u7684\u8868\u793a
9.1.2\u56fe\u80fd\u89e3\u51b3\u7684\u95ee\u9898
9.2 \u56fe\u795e\u7ecf\u7f51\u7edc\u6a21\u578b
9.2.1 \u7b80\u5355\u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5b9e\u73b0\u548c\u4f7f\u7528
9.2.2 \u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u5c42\u7ed3\u6784\u4e0e\u8fde\u63a5\u6027
9.2.3\u56fe\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u7684\u8bad\u7ec3
9.2.4\u56fe\u6570\u636e\u7684\u6570\u636e\u589e\u5f3a
9.2.5\u5c0f\u7ed3
9.3 \u56fe\u795e\u7ecf\u7f51\u7edc\u7b97\u6cd5\u57fa\u7840\u7684\u53d8\u4f53
9.3.1 Graph Convolutional Network\uff08GCN\uff09
9.3.2 Graph Sample and Aggregate Network\uff08GraphSAGE\uff09
9.3.3 Graph Attention Network
\u7b2c\u5341\u7ae0\uff1a\u5f3a\u5316\u5b66\u4e60
10.1\u5f3a\u5316\u5b66\u4e60\u57fa\u7840\u6982\u5ff5
10.1.1 \u6982\u8ff0
10.1.2 \u5f3a\u5316\u5b66\u4e60\u57fa\u672c\u6982\u5ff5
10.1.3 \u7406\u89e3\u5f3a\u5316\u5b66\u4e60\u4e2d\u7684\u968f\u673a\u6027
10.2\u57fa\u4e8e\u4ef7\u503c\u7684\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\uff08DQN\uff09
10.2.1 DQN\u4ecb\u7ecd
10.2.2 \u8d1d\u5c14\u66fc\u65b9\u7a0b\u4e0e\u65f6\u5e8f\u5dee\u5206\u5b66\u4e60\uff08TD-Learning\uff09
10.2.3 \u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc
10.2.4 \u4f30\u8ba1\u7f51\u7edc\u4e0e\u76ee\u6807\u7f51\u7edc
10.3\u57fa\u4e8e\u7b56\u7565\u7684\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60
10.3.1 \u7b97\u6cd5\u4ecb\u7ecd\uff1a\u57fa\u4e8e\u7b56\u7565\u7684\u5f3a\u5316\u5b66\u4e60
10.3.2 \u7b56\u7565\u4f18\u5316
10.3.3 \u5bf9\u6bd4\u68af\u5ea6\u4e0a\u5347\u548c\u65f6\u5e8f\u5dee\u5206
10.4\u6f14\u5458-\u8bc4\u8bba\u5bb6\u6a21\u578b
10.4.1 \u7b97\u6cd5\u4ecb\u7ecd\uff1a\u6f14\u5458-\u8bc4\u8bba\u5bb6\uff08Actor-Critic\uff09\u6a21\u578b
10.4.2 \u6f14\u5458-\u8bc4\u8bba\u5bb6\u6a21\u578b\u7b97\u6cd5\u8bad\u7ec3
10.4.3 \u6f14\u5458-\u8bc4\u8bba\u5bb6\u6a21\u578b\u7b97\u6cd5\u7684\u4f18\u7f3a\u70b9
10.4.4 \u5bf9\u6bd4\u751f\u6210\u5bf9\u6297\u7f51\u7edc\u548c\u6f14\u5458-\u8bc4\u8bba\u5bb6\u6a21\u578b\u7b97\u6cd5 <\/p>\n\n\n\n
\u611f\u8c22\u6240\u6709\u652f\u6301\u6211\u3001\u9f13\u52b1\u6211\u548c\u4e3a\u8fd9\u672c\u4e66\u63d0\u4f9b\u5b9d\u8d35\u5efa\u8bae\u7684\u4eba\u3002\u6ca1\u6709\u4f60\u4eec\uff0c\u8fd9\u672c\u4e66\u5c06\u4e0d\u4f1a\u6210\u4e3a\u73b0\u5b9e\u3002<\/p>\n\n\n\n
\u611f\u8c22\u4f5f\u5929\u65ed\uff0c\u5f90\u6893\u6e90\u5bf9\u672c\u4e66\u5185\u5bb9\u7684\u6574\u7406\uff0c\u6821\u5bf9\u548c\u4fee\u6539\u3002<\/p>\n\n\n\n
\u611f\u8c22\u6211\u7684\u59bb\u5b50\u548c\u5bb6\u4eba\uff0c\u5728\u6211\u5199\u4f5c\u8fc7\u7a0b\u4e2d\u627f\u62c5\u4e86\u751f\u6d3b\u7684\u7410\u4e8b\uff0c\u4f7f\u6211\u53ef\u4ee5\u5168\u8eab\u5fc3\u5730\u6295\u5165\u5199\u4f5c\u5de5\u4f5c\u3002 \u7531\u4e8e\u65f6\u95f4\u4ed3\u4fc3\uff0c\u4e66\u4e2d\u96be\u514d\u5b58\u5728\u4e0d\u59a5\u4e4b\u5904\uff0c\u8bf7\u8bfb\u8005\u89c1\u8c05\uff0c\u5e76\u63d0\u5b9d\u8d35\u610f\u89c1\u3002<\/p>\n\n\n\n
[\u4e8e\u6d69\u6587]<\/p>\n\n\n\n
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