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<\/p>\n

\uc548\ub155\ud558\uc138\uc694. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud000\ud2b8\ub9e4\ub9e4\ub97c \uc704\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uc21c\uc804\ud30c(Forward Propagation)\uc5d0 \ub300\ud55c \ub0b4\uc6a9\uc744 \uc911\uc810\uc801\uc73c\ub85c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ub9e4\ucee4\ub2c8\uc998\uc740 \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \ub9c8\uc2a4\ud130\ud558\uba74 \uc5ec\ub7ec\ubd84\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \ud55c \ub2e8\uacc4 \uc5c5\uadf8\ub808\uc774\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774\ub780?<\/h2>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uc7a5\uc5d0\uc11c \uac70\ub798\ub97c \uc790\ub3d9\uc73c\ub85c \uc218\ud589\ud558\ub294 \uc5f0\uc18d\uc801\uc778 \uc804\uc790 \uac70\ub798 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc740 \uc54c\uace0\ub9ac\uc998\uc744 \uae30\ubc18\uc73c\ub85c \ud558\uc5ec \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uba74 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \uc778\uc2dd\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \ubcf4\ub2e4 \uc815\uad50\ud55c \uac70\ub798 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1.1 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc7a5\uc810<\/h3>\n

\uac10\uc815 \ubc30\uc81c: \ucef4\ud4e8\ud130\ub294 \uac10\uc815\uc744 \ub290\ub07c\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \uc778\ub0b4\uc2ec\uc744 \uac00\uc9c0\uace0 \uac70\ub798\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\uc2e0\uc18d\ud55c \uc2e4\ud589: \uc54c\uace0\ub9ac\uc998\uc740 \uc2dc\uc7a5 \ubcc0\ud654\uc5d0 \uc2e0\uc18d\ud558\uac8c \ubc18\uc751\uc774 \uac00\ub2a5\ud558\uc5ec \uc6b0\uc704\ub97c \uc810\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\ub370\uc774\ud130 \ubd84\uc11d: \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \uc0ac\ub78c\ubcf4\ub2e4 \ub354 \ub098\uc740 \ud310\ub2e8\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n

\ubc31\ud14c\uc2a4\ud305: \uc54c\uace0\ub9ac\uc998\uc740 \uacfc\uac70 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc2dc\ubbac\ub808\uc774\uc158\ud558\uc5ec \uc804\ub7b5\uc758 \uc720\ud6a8\uc131\uc744 \uac80\uc99d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n

2. \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \ucef4\ud4e8\ud130 \uacfc\ud559\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \uc2dd\ubcc4\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \ubbf8\ub798 \uacb0\uacfc\ub97c \uc608\uce21\ud558\uae30 \uc704\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud569\ub2c8\ub2e4. \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \ubc29\uc2dd\uc73c\ub85c \ud65c\uc6a9\ub420 \uc218 \uc788\uc73c\uba70, \uac00\uaca9 \uc608\uce21, \ub9ac\uc2a4\ud06c \uad00\ub9ac \ubc0f \ud22c\uc790 \uc804\ub7b5 \uac1c\ubc1c \ub4f1 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c \uc751\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

2.1 \uc9c0\ub3c4 \ud559\uc2b5 vs \ube44\uc9c0\ub3c4 \ud559\uc2b5<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \ud06c\uac8c \uc9c0\ub3c4 \ud559\uc2b5\uacfc \ube44\uc9c0\ub3c4 \ud559\uc2b5\uc73c\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\uc9c0\ub3c4 \ud559\uc2b5(Supervised Learning):<\/strong> \uc785\ub825 \ub370\uc774\ud130\uc640 \uc815\ub2f5 \ub808\uc774\ube14\uc774 \uc8fc\uc5b4\uc9c8 \ub54c, \ubaa8\ub378\uc774 \uc774\ub97c \ud559\uc2b5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21\uc5d0\uc11c\ub294 \uacfc\uac70 \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\uc640 \ud574\ub2f9\ud558\ub294 \uc815\ud655\ud55c \uc8fc\uc2dd \uac00\uaca9\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n

\ube44\uc9c0\ub3c4 \ud559\uc2b5(Unsupervised Learning):<\/strong> \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub808\uc774\ube14\uc774 \uc5c6\uc744 \ub54c, \ub370\uc774\ud130\uc758 \ud328\ud134 \ub610\ub294 \uad6c\uc870\ub97c \ucc3e\uae30 \uc704\ud574 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc8fc\uc2dd \uc2dc\uc7a5\uc758 \uad70\uc9d1\ud654(clustering)\ub098 \ucc28\uc6d0 \ucd95\uc18c(dimensionality reduction)\uc640 \uac19\uc740 \uae30\uc220\uc774 \ube44\uc9c0\ub3c4 \ud559\uc2b5\uc5d0 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n
3. \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd(Artificial Neural Networks)\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uae30\uc220\uc785\ub2c8\ub2e4. \ub2e4\uce35 \uc2e0\uacbd\ub9dd(multi-layer neural networks)\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \uc790\ub3d9\uc73c\ub85c \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \uc608\uce21 \ub610\ub294 \ubd84\ub958 \uc791\uc5c5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \ud2b9\ud788 \ubcf5\uc7a1\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70, \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc131\uacf5\uc801\uc73c\ub85c \uc751\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \uc2e0\uacbd\ub9dd\uc758 \uad6c\uc870<\/h3>\n
\uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uce35, \uc740\ub2c9\uce35(hidden layer), \ucd9c\ub825\uce35\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.<\/p>\n
\n
\uc785\ub825\uce35(Input Layer):<\/strong> \ubaa8\ub378\uc5d0 \uc81c\uacf5\ub418\ub294 \ub370\uc774\ud130\uc758 \ud2b9\uc131\uc774 \uc785\ub825\ub418\ub294 \uce35\uc785\ub2c8\ub2e4. \uac70\ub798\uc5d0\uc11c\ub294 \uc8fc\uac00, \uac70\ub798\ub7c9, \ub274\uc2a4 \ub370\uc774\ud130 \ub4f1\uc774 \uc785\ub825\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\uc740\ub2c9\uce35(Hidden Layer):<\/strong> \uc785\ub825 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \uc911\uac04 \uce35\uc785\ub2c8\ub2e4. \uc5ec\ub7ec \uac1c\uc758 \uc740\ub2c9\uce35\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub294 \ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uc744 \uc99d\uac00\uc2dc\ucf1c \ub354 \uc815\uad50\ud55c \ud559\uc2b5\uc774 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/li>\n
\ucd9c\ub825\uce35(Output Layer):<\/strong> \ubaa8\ub378\uc758 \ucd5c\uc885 \uacb0\uacfc\uac00 \ucd9c\ub825\ub418\ub294 \uce35\uc785\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9, \uc8fc\uc2dd\uc758 \ub9e4\uc218\/\ub9e4\ub3c4 \uacb0\uc815\uc774 \uc5ec\uae30\uc5d0 \ud574\ub2f9\ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n
4. \uc21c\uc804\ud30c(Forward Propagation)<\/h2>\n
\uc21c\uc804\ud30c\ub294 \uc2e0\uacbd\ub9dd\uc758 \uc785\ub825 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uc5ec \uacb0\uacfc\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 \ub9d0\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \uac01 \uce35\uc758 \ub274\ub7f0\uc774 \uc774\uc804 \uce35\uc758 \ub274\ub7f0\uc73c\ub85c\ubd80\ud130 \ubc1b\uc544\uc628 \uc785\ub825\uc5d0 \uac00\uc911\uce58(weight)\ub97c \uacf1\ud558\uace0, \ud3b8\ud5a5(bias)\uc744 \ub354\ud55c \ud6c4 \ud65c\uc131\ud654 \ud568\uc218(activation function)\ub97c \ud1b5\uacfc\uc2dc\ucf1c \ucd5c\uc885 \ucd9c\ub825\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
4.1 \uc21c\uc804\ud30c \ub2e8\uacc4<\/h3>\n
\n
\uc785\ub825\uac12 \uc900\ube44:<\/strong> \ubaa8\ub378\uc5d0 \uc785\ub825\ub420 \ud2b9\uc131 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4.<\/li>\n
\uac00\uc911\uce58\uc640 \ud3b8\ud5a5 \uc801\uc6a9:<\/strong> \uac01 \uc785\ub825\uac12\uc740 \ud574\ub2f9 \uac00\uc911\uce58\uc640 \uacf1\ud574\uc838 \ub204\uc801\ub429\ub2c8\ub2e4. \uadf8\ub7f0 \ub2e4\uc74c \ud3b8\ud5a5\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.<\/li>\n
\ud65c\uc131\ud654 \ud568\uc218 \uc801\uc6a9:<\/strong> \uac00\uc911\uce58\uc640 \ud3b8\ud5a5\uc758 \ud569\uc5d0 \ud65c\uc131\ud654 \ud568\uc218\ub97c \uc801\uc6a9\ud558\uc5ec \ucd9c\ub825\uac12\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \ud65c\uc131\ud654 \ud568\uc218\ub85c\ub294 \uc2dc\uadf8\ubaa8\uc774\ub4dc(Sigmoid), \ub810\ub8e8(ReLU), \ud0c4\uc820\ud2b8 \ud558\uc774\ud37c\ubcfc\ub9ad(tanh) \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\ucd9c\ub825\uac12 \uc0dd\uc131:<\/strong> \ub9c8\uc9c0\ub9c9 \ucd9c\ub825\uce35\uc5d0\uc11c \ucd5c\uc885 \uc608\uce21 \uacb0\uacfc\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
4.2 \uc218\ud559\uc801 \ud45c\ud604<\/h3>\n
\uac00\uc911\uce58\ub97c W, \ud3b8\ud5a5\uc744 b, \uc785\ub825\uac12\uc744 X\ub85c \ub450\uc5c8\uc744 \ub54c, \ud55c \ub274\ub7f0\uc758 \ucd9c\ub825 Y\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n Y = activation(W * X + b)\n <\/pre>\n
4.3 \ud65c\uc131\ud654 \ud568\uc218\uc758 \uc911\uc694\uc131<\/h3>\n
\ud65c\uc131\ud654 \ud568\uc218\ub294 \uc2e0\uacbd\ub9dd\uc758 \ube44\uc120\ud615\uc131\uc744 \ub3c4\uc785\ud558\uc5ec \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uac8c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub810\ub8e8 \ud568\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uacf5\uc2dd\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n f(x) = max(0, x)\n <\/pre>\n
\uc774 \ud568\uc218\ub294 \uc74c\uc218 \uc785\ub825\uc5d0 \ub300\ud574 0\uc744 \ucd9c\ub825\ud558\ubbc0\ub85c, \ube44\uc120\ud615\uc801\uc778 \ud2b9\uc131\uc744 \uc720\uc9c0\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc758 \ud45c\ud604\ub825\uc744 \ub192\uc774\ub294 \ub370 \uae30\uc5ec\ud569\ub2c8\ub2e4.<\/p>\n
5. \uc2e0\uacbd\ub9dd \ud6c8\ub828\uacfc \uc5ed\uc804\ud30c<\/h2>\n
\uc21c\uc804\ud30c\ub85c \uc0dd\uc131\ub41c \ucd9c\ub825\uac12\uacfc \uc2e4\uc81c \ub808\uc774\ube14 \uac04\uc758 \uc624\ucc28\ub97c \uae30\ubc18\uc73c\ub85c \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc5ed\uc804\ud30c(Backpropagation) \uae30\uc220\uc774 \ub4f1\uc7a5\ud558\uc5ec, \uc624\ucc28\ub97c \ub124\ud2b8\uc6cc\ud06c\uc758 \uac01 \uac00\uc911\uce58\uc640 \uc5f0\uacb0\uc5d0 \ub530\ub77c \ubd84\ubc30\ud558\uc5ec \uac00\uc911\uce58\ub97c \uc870\uc815\ud569\ub2c8\ub2e4.<\/p>\n
5.1 \uc190\uc2e4 \ud568\uc218( Loss Function)<\/h3>\n
\uc190\uc2e4 \ud568\uc218\ub294 \ubaa8\ub378\uc758 \uc608\uce21\uac12\uacfc \uc2e4\uc81c \uac12 \uac04\uc758 \ucc28\uc774\ub97c \uce21\uc815\ud558\ub294 \ud568\uc218\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \ud615\ud0dc\ub97c \uac00\uc9d1\ub2c8\ub2e4:<\/p>\n
\n Loss(y_true, y_pred) = (y_true - y_pred)^2\n <\/pre>\n
5.2 \uac00\uc911\uce58 \uc5c5\ub370\uc774\ud2b8<\/h3>\n
\uc5ed\uc804\ud30c\ub97c \ud1b5\ud574 \uad6c\ud55c \uc624\ucc28\uc758 \uae30\uc6b8\uae30\ub97c \ubc14\ud0d5\uc73c\ub85c \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4. \uacbd\ub7c9 \ud558\uac15(Gradient Descent) \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac01 \uac00\uc911\uce58\ub97c \ub2e4\uc74c\uacfc \uac19\uc774 \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4:<\/p>\n
\n W_new = W_old - learning_rate * gradient\n <\/pre>\n
\uc5ec\uae30\uc11c learning_rate\ub294 \uac00\uc911\uce58\ub97c \uc870\uc815\ud558\ub294 \uc18d\ub3c4\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/p>\n
6. \uc2e4\uc81c \uc608\uc81c: \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21<\/h2>\n
\uc774\uc81c \ubcf8\uaca9\uc801\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 \uacfc\uac70 \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ud558\uc5ec \ubbf8\ub798 \uac00\uaca9\uc744 \uc608\uce21\ud558\ub294 \ub2e8\uc21c\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
6.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\ub294 Yahoo Finance API\ub098 \ub2e4\ub978 \uae08\uc735 \ub370\uc774\ud130 \uc81c\uacf5 \uc11c\ube44\uc2a4\ub97c \ud1b5\ud574 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc218\uc9d1\ub41c \ub370\uc774\ud130\ub294 \uc804\ucc98\ub9ac \uacfc\uc815\uc774 \ud544\uc694\ud558\uba70, \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4:<\/p>\n
\n
\ub0a0\uc9dc, \uc885\uac00, \uac70\ub798\ub7c9 \ub4f1\uc744 \uc120\ud0dd\ud558\uc5ec \ub370\uc774\ud130 \ud504\ub808\uc784\uc73c\ub85c \uad6c\uc131.<\/li>\n
\uacb0\uce21\uce58 \ucc98\ub9ac \ubc0f \uc815\uaddc\ud654 \uc9c4\ud589.<\/li>\n
\ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \ubd84\ud560.<\/li>\n<\/ul>\n
6.2 \ubaa8\ub378 \uc124\uacc4 \ubc0f \uad6c\ud604<\/h3>\n
\uac04\ub2e8\ud55c \uc21c\uc804\ud30c \uae30\ubc18\uc758 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc124\uacc4\ud569\ub2c8\ub2e4. Python\uc758 Keras\uc640 TensorFlow \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc544\ub798\uc640 \uac19\uc740 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n from keras.models import Sequential\n from keras.layers import Dense\n\n model = Sequential()\n model.add(Dense(units=64, activation='relu', input_dim=number_of_features))\n model.add(Dense(units=32, activation='relu'))\n model.add(Dense(units=1, activation='linear'))\n\n model.compile(optimizer='adam', loss='mean_squared_error')\n model.fit(X_train, y_train, epochs=100, batch_size=32)\n <\/pre>\n
6.3 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \uc2e4\uc81c \uac12\uacfc\uc758 \ucc28\uc774\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Matplotlib \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc74c\uacfc \uac19\uc740 \ucc28\ud2b8\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n import matplotlib.pyplot as plt\n\n plt.plot(y_test, label='True Price')\n plt.plot(predictions, label='Predicted Price')\n plt.legend()\n plt.show()\n <\/pre>\n
7. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud2b9\ud788 \uc21c\uc804\ud30c\uc758 \uac1c\ub150\uacfc \uc2e4\uc81c \uc608\uc81c\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ubc95\ub4e4\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ub370\uc774\ud130 \uae30\ubc18\uc758 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \uc788\uc5b4 \ud070 \ub3c4\uc6c0\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4. \uc7a5\uae30\uc801\uc73c\ub85c \uc5ec\ub7ec\ubd84\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uacfc \ud22c\uc790 \uc131\uacfc\ub97c \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\ub294 \uae30\ubc18\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uc790\uc2e0\ub9cc\uc758 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud574 \ub098\uac00\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
8. \ucc38\uace0\uc790\ub8cc<\/h2>\n
\n
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning”, MIT Press.<\/li>\n
Alexander Elder, “Trading for a Living”.<\/li>\n
Andreas C. M\u00fcller and Sarah Guido, “Introduction to Machine Learning with Python”.<\/li>\n<\/ul>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc548\ub155\ud558\uc138\uc694. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud000\ud2b8\ub9e4\ub9e4\ub97c \uc704\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uc21c\uc804\ud30c(Forward Propagation)\uc5d0 \ub300\ud55c \ub0b4\uc6a9\uc744 \uc911\uc810\uc801\uc73c\ub85c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ub9e4\ucee4\ub2c8\uc998\uc740 \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \ub9c8\uc2a4\ud130\ud558\uba74 \uc5ec\ub7ec\ubd84\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \ud55c \ub2e8\uacc4 \uc5c5\uadf8\ub808\uc774\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774\ub780? \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc744 \uc0ac\uc6a9\ud558\uc5ec … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc21c\uc804\ud30c”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-29111","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc21c\uc804\ud30c - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/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:\/\/atmokpo.com\/w\/29111\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc21c\uc804\ud30c - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc548\ub155\ud558\uc138\uc694. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud000\ud2b8\ub9e4\ub9e4\ub97c \uc704\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uc21c\uc804\ud30c(Forward Propagation)\uc5d0 \ub300\ud55c \ub0b4\uc6a9\uc744 \uc911\uc810\uc801\uc73c\ub85c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ub9e4\ucee4\ub2c8\uc998\uc740 \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \ub9c8\uc2a4\ud130\ud558\uba74 \uc5ec\ub7ec\ubd84\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \ud55c \ub2e8\uacc4 \uc5c5\uadf8\ub808\uc774\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774\ub780? \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc744 \uc0ac\uc6a9\ud558\uc5ec … \ub354 \ubcf4\uae30 "\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc21c\uc804\ud30c"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29111\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-27T16:16:50+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:54:25+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29111\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29111\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc21c\uc804\ud30c\",\"datePublished\":\"2024-10-27T16:16:50+00:00\",\"dateModified\":\"2024-11-26T06:54:25+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29111\/\"},\"wordCount\":81,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29111\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29111\/\",\"name\":\"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc21c\uc804\ud30c - 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