{"id":29383,"date":"2024-10-27T16:18:10","date_gmt":"2024-10-27T16:18:10","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29383"},"modified":"2024-11-26T06:52:48","modified_gmt":"2024-11-26T06:52:48","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-%ed%85%90%ec%84%9c%ed%94%8c%eb%a1%9c-2%eb%a5%bc-5","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29383\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud150\uc11c\ud50c\ub85c 2\ub97c \uc774\uc6a9\ud55c \uc2dc\uacc4\uc5f4\uc744 \uc704\ud55c RNN"},"content":{"rendered":"

<\/p>\n

\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \ud604\uc7ac \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud55c \ud601\uc2e0\uc744 \uc8fc\ub3c4\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc740 \ud22c\uc790\uc5d0\uc11c \uc911\uc694\ud55c \uc694\uc18c\uc774\uba70, RNN(\uc21c\ud658 \uc2e0\uacbd\ub9dd)\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud150\uc11c\ud50c\ub85c 2\ub97c \uc0ac\uc6a9\ud558\uc5ec RNN\uc744 \uc774\uc6a9\ud55c \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uac1c\ub150<\/h2>\n

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uc7a5\uc5d0\uc11c \ub9e4\ub9e4 \uacb0\uc815\uc744 \uc790\ub3d9\uc73c\ub85c \uc218\ud589\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d, \ud22c\uc790 \uc804\ub7b5 \uac1c\ubc1c, \uadf8\ub9ac\uace0 \uc790\ub3d9\ud654\ub41c \ub9e4\ub9e4 \uc2e4\ud589\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc8fc\uc694 \uc7a5\uc810 \uc911 \ud558\ub098\ub294 \ube60\ub978 \uacb0\uc815\uacfc \uc2e4\ud589 \uc18d\ub3c4\uc785\ub2c8\ub2e4.<\/p>\n

2. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ucc28\uc774<\/h2>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \uae30\uacc4\uac00 \uacbd\ud5d8\uc744 \ud1b5\ud574 \ud559\uc2b5\ud558\uc5ec \ud2b9\uc815 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \ub9d0\ud569\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube44\uc120\ud615\uc801\uc778 \uad00\uacc4\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \ud070 \ub370\uc774\ud130 \uc138\ud2b8\uc640 \ubcf5\uc7a1\ud55c \ubb38\uc81c \ud574\uacb0\uc5d0 \uc801\ud569\ud55c \ub9ce\uc740 \ub808\uc774\uc5b4\ub97c \uac00\uc9c4 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n

3. \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \uc774\ud574<\/h2>\n

\uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub294 \uc2dc\uac04\uc5d0 \ub530\ub77c \uc815\ub82c\ub41c \ub370\uc774\ud130\ub97c \ub9d0\ud569\ub2c8\ub2e4. \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc8fc\uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \ud658\uc728 \ub4f1 \ub2e4\uc591\ud55c \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub294 \uc2dc\uac04\uc5d0 \ub530\ub978 \ud328\ud134\uc744 \ubd84\uc11d\ud558\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2dc\uacc4\uc5f4 \ubd84\uc11d\uc758 \uc8fc\uc694 \ubaa9\ud45c\ub294 \uacfc\uac70 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ubbf8\ub798 \uac12\uc744 \uc608\uce21\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n

4. RNN\uc758 \uc6d0\ub9ac<\/h2>\n

RNN(\uc21c\ud658 \uc2e0\uacbd\ub9dd)\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc640 \uac19\uc740 \uc21c\ucc28\uc801 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uae30 \uc704\ud574 \uc124\uacc4\ub41c \uc2e0\uacbd\ub9dd\uc758 \ud55c \uc885\ub958\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \uc2e0\uacbd\ub9dd\uc740 \uc785\ub825 \ub370\uc774\ud130\uc758 \uace0\uc815\ub41c \ud06c\uae30\ub85c \ud328\ud134\uc744 \ucd94\ucd9c\ud558\ub294 \ub370 \ubc18\ud574, RNN\uc740 \uc774\uc804 \ub2e8\uacc4\uc758 \ucd9c\ub825\uc744 \ub2e4\uc74c \ub2e8\uacc4\uc758 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud568\uc73c\ub85c\uc368 \ub370\uc774\ud130\ub97c \uc5f0\uc18d\uc801\uc73c\ub85c \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud2b9\uc131 \ub355\ubd84\uc5d0 RNN\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \uc2dc\uac04\uc801 \uc758\uc874\uc131\uc744 \uc798 \ubaa8\ub378\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

4.1 RNN\uc758 \uad6c\uc870<\/h3>\n

RNN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ubcf8 \uad6c\uc870\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

\n    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n    \u2502  h\u1d62\u208b\u2081   \u2502   \u2190 \uc774\uc804 \uc0c1\ud0dc\n    \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2518\n          \u2502\n    \u250c\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2510\n    \u2502  h\u1d62  (\ud604\uc7ac \uc0c1\ud0dc) \u2502\n    \u2514\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2518\n          \u2502\n    \u250c\u2500\u2500\u2500\u2500\u2500\u25bc\u2500\u2500\u2500\u2500\u2500\u2510\n    \u2502  y\u1d62  (\ucd9c\ub825\uac12)     \u2502\n    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n<\/pre>\n
4.2 RNN\uc758 \ud559\uc2b5 \ubc29\uc2dd<\/h3>\n
RNN\uc740 \uc8fc\ub85c ‘\uc5ed\uc804\ud30c(Backpropagation)’\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774 \uacfc\uc815\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 ‘\uae30\uc6b8\uae30 \uc18c\uc2e4(Vanishing Gradient)’ \ubb38\uc81c\ub85c \uc778\ud574 \uae34 \uc2dc\ud000\uc2a4\ub97c \ud559\uc2b5\ud558\ub294 \ub370 \uc5b4\ub824\uc6c0\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 ‘LSTM(Long Short-Term Memory)’\uc774\ub098 ‘GRU(Gated Recurrent Unit)’\uc640 \uac19\uc740 \uc218\uc815\ub41c RNN \uad6c\uc870\uac00 \uc8fc\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
5. \ud150\uc11c\ud50c\ub85c 2 \uc124\uce58<\/h2>\n
\ud150\uc11c\ud50c\ub85c 2\ub294 \uad6c\uae00\uc5d0\uc11c \uac1c\ubc1c\ud55c \ub525\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. TensorFlow\ub97c \uc124\uce58\ud558\uae30 \uc704\ud574\uc11c\ub294 Python\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec TensorFlow\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install tensorflow<\/code><\/pre>\n
6. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
\uc774\uc81c \uc2e4\uc81c \ub370\uc774\ud130\ub85c \uc791\uc5c5\uc744 \uc2dc\uc791\ud560 \uc900\ube44\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\ub294 Yahoo Finance \ub610\ub294 \ub2e4\ub978 \uae08\uc735 \ub370\uc774\ud130 \uc81c\uacf5 \uc0ac\uc774\ud2b8\uc5d0\uc11c CSV \ud3ec\ub9f7\uc73c\ub85c \ub2e4\uc6b4\ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ud615\uc2dd\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4:<\/p>\n
\nDate,Open,High,Low,Close,Volume\n2023-01-01,100.0,101.0,99.0,100.5,10000\n2023-01-02,100.5,102.5,99.5,101.0,12000\n...\n<\/code><\/pre>\n
6.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc6d0raw data\ub97c \ubaa8\ub378\uc5d0 \uc801\ud569\ud55c \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ub2e4\uc74c\uc758 \uc8fc\uc694 \uacfc\uc815\uc744 \ud3ec\ud568\ud560 \uc608\uc815\uc785\ub2c8\ub2e4:<\/p>\n
    \n
  1. \ubd88\ud544\uc694\ud55c \uc5f4 \uc81c\uac70: \ub0a0\uc9dc\uc640 \uac19\uc740 \ud544\uc694 \uc5c6\ub294 \uc815\ubcf4\ub97c \uc81c\uac70\ud569\ub2c8\ub2e4.<\/li>\n
  2. \uc815\uaddc\ud654: \uac00\uaca9 \ub370\uc774\ud130\ub97c 0\uacfc 1 \uc0ac\uc774\uc758 \uac12\uc73c\ub85c \ubcc0\ud658\ud558\uc5ec \ud559\uc2b5\uc744 \ub3d5\uc2b5\ub2c8\ub2e4.<\/li>\n
  3. \uc0d8\ud50c \ub370\uc774\ud130 \uc0dd\uc131: \ubaa8\ub378 \ud559\uc2b5\uc5d0 \uc801\ud569\ud55c \ud615\ud0dc\ub85c \ub370\uc774\ud130\ub97c \ub098\ub215\ub2c8\ub2e4.<\/li>\n<\/ol>\n
    6.2 \ud30c\uc774\uc36c \ucf54\ub4dc\ub85c \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
    \uc544\ub798\ub294 \uac04\ub2e8\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n
    \nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv')\n\n# \ud544\uc694 \uc5c6\ub294 \uc5f4 \uc81c\uac70\ndata = data[['Date', 'Close']]\n\n# \uc815\uaddc\ud654\nscaler = MinMaxScaler(feature_range=(0, 1))\ndata['Close'] = scaler.fit_transform(data['Close'].values.reshape(-1, 1))\n\n# \ub370\uc774\ud130 \uc2dc\ud000\uc2a4 \uc0dd\uc131\ndef create_dataset(data, time_step=1):\n    X, y = [], []\n    for i in range(len(data) - time_step - 1):\n        X.append(data[i:(i + time_step), 0])\n        y.append(data[i + time_step, 0])\n    return np.array(X), np.array(y)\n\ndata = data['Close'].values\nX, y = create_dataset(data, time_step=10)\nX = X.reshape(X.shape[0], X.shape[1], 1)\n<\/code><\/pre>\n
    7. RNN \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
    \uc774\uc81c \uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc778 RNN\uc744 \ud150\uc11c\ud50c\ub85c 2\ub85c \uad6c\ud604\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
    \nimport numpy as np\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, LSTM, Dropout\n\n# \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(units=50, return_sequences=False))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(units=1))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n<\/code><\/pre>\n
    8. \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
    \ubaa8\ub378\uc758 \ud559\uc2b5\uc744 \uc2dc\uc791\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub192\uc774\uae30 \uc704\ud574 \uc801\uc808\ud55c \uc5d0\ud3ed\uacfc \ubc30\uce58 \ud06c\uae30\ub97c \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4:<\/p>\n
    \n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X, y, epochs=100, batch_size=32)\n<\/code><\/pre>\n
    9. \uacb0\uacfc \uc608\uce21 \ubc0f \uc2dc\uac01\ud654<\/h2>\n
    \ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \ud6c4 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4:<\/p>\n
    \nimport matplotlib.pyplot as plt\n\n# \uc608\uce21\npredictions = model.predict(X)\n\n# \uc6d0\ub798 \uc2a4\ucf00\uc77c\ub85c \ubcc0\ud658\npredictions = scaler.inverse_transform(predictions)\n\n# \uc2dc\uac01\ud654\nplt.figure(figsize=(10,6))\nplt.plot(data, color='red', label='Actual Price')\nplt.plot(predictions, color='blue', label='Predicted Price')\nplt.title('Stock Price Prediction')\nplt.xlabel('Time')\nplt.ylabel('Stock Price')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
    10. \uace0\uae09 \ubaa8\ub378 \ud29c\ub2dd<\/h2>\n
    RNN\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd \ubc0f \ucd94\uac00 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
      \n
    1. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815: \ubc30\uce58 \ud06c\uae30, \uc5d0\ud3ed, \ub808\uc774\uc5b4 \uc218 \ubc0f \uc720\ub2db \uc218 \ub4f1\uc744 \uc870\uc815\ud569\ub2c8\ub2e4.<\/li>\n
    2. \uc815\uaddc\ud654 \uae30\ubc95 \uc801\uc6a9: \ub4dc\ub86d\uc544\uc6c3(Dropout), \uac00\uc911\uce58 \uac10\uc18c(Weight Regularization) \ub4f1\uc744 \uc801\uc6a9\ud558\uc5ec \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud569\ub2c8\ub2e4.<\/li>\n
    3. \ub2e4\uc591\ud55c RNN \uad6c\uc870 \uc2e4\ud5d8: LSTM, GRU \uc678\uc5d0\ub3c4 \ub2e4\uc591\ud55c \uc544\ud0a4\ud14d\ucc98\ub97c \uc2e4\ud5d8\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
      11. \uacb0\ub860<\/h2>\n
      \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \ud604\ub300 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \ud544\uc218\uc801\uc778 \uc694\uc18c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. RNN\uc744 \uc774\uc6a9\ud55c \uc2dc\uacc4\uc5f4 \uc608\uce21\uc740 \ub9e4\uc6b0 \uc720\ub9dd\ud55c \ubd84\uc57c\uc774\uba70, \ud150\uc11c\ud50c\ub85c 2\ub97c \ud65c\uc6a9\ud558\uc5ec \ud6a8\uacfc\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \uae30\ubcf8\uc801\uc778 RNN \ubaa8\ub378 \uad6c\ucd95\uacfc \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc608\uce21 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc774\ud574\ud558\uc168\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
      \uc774 \uae00\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \uad00\uc2ec\uc774 \uc788\ub294 \ubaa8\ub4e0 \ubd84\ub4e4\uc5d0\uac8c \uc720\uc6a9\ud55c \uc790\ub8cc\uac00 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \ucd94\uac00\uc801\uc778 \ud559\uc2b5\uc744 \uc704\ud574 TensorFlow \uacf5\uc2dd \ubb38\uc11c \ubc0f \uad00\ub828 \uc11c\uc801\uc744 \ucc38\uace0\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
      <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
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