{"id":29421,"date":"2024-10-27T16:18:22","date_gmt":"2024-10-27T16:18:22","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29421"},"modified":"2024-11-26T06:52:41","modified_gmt":"2024-11-26T06:52:41","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%8c%8c%ec%9d%b4%ec%8d%ac%ec%9d%84-%ec%82%ac","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29421\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud574 \uc5ed\uc804\ud30c\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><body><\/p>\n<div class=\"section\">\n<p>\ud604\ub300 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774 \uc810\uc810 \ub354 \ubcf4\ud3b8\ud654\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \ub2a5\ub825\uc774 \ud5a5\uc0c1\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 Python\uc744 \uc0ac\uc6a9\ud558\uace0, \uc5ed\uc804\ud30c(Backpropagation) \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<\/div>\n<div class=\"section\">\n<h2>\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubcf8 \uac1c\ub150<\/h2>\n<p>\uba38\uc2e0\ub7ec\ub2dd(Machine Learning)\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uace0 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd(Deep Learning)\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \ub354 \uae4a\uc774 \uc788\ub294 \ud328\ud134\uc744 \uc2dd\ubcc4\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li><strong>\uc9c0\ub3c4\ud559\uc2b5(Supervised Learning)<\/strong>: \uc785\ub825\uacfc \ucd9c\ub825 \ub370\uc774\ud130\uac00 \uc8fc\uc5b4\uc9c4 \uc0c1\ud0dc\uc5d0\uc11c \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ube44\uc9c0\ub3c4\ud559\uc2b5(Unsupervised Learning)<\/strong>: \ucd9c\ub825 \ub370\uc774\ud130 \uc5c6\uc774 \uc785\ub825 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uac15\ud654\ud559\uc2b5(Reinforcement Learning)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uc5d0\uc11c \ud589\ub3d9\uc744 \ud558\uc5ec \ubcf4\uc0c1\uc744 \ucd5c\ub300\ud654\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<\/div>\n<div class=\"section\">\n<h2>\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08<\/h2>\n<p>\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \ub9e4\uc218 \ub610\ub294 \ub9e4\ub3c4 \uacb0\uc815\uc744 \uc790\ub3d9\uc73c\ub85c \ub0b4\ub9ac\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc790\ub3d9\ud654\ub41c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc740 \uc5ec\ub7ec \uac00\uc9c0 \uc7a5\uc810\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\uc815\ud655\ud55c \ub370\uc774\ud130 \ubd84\uc11d\uacfc \ud1b5\uacc4\uc801 \uc758\uc0ac\uacb0\uc815<\/li>\n<li>\uac10\uc815\uc758 \ubc30\uc81c: \uae30\uacc4\uc801 \uc811\uadfc\uc73c\ub85c \uac10\uc815\uc801 \uacb0\uc815 \ud68c\ud53c<\/li>\n<li>\uace0\uc18d \uac70\ub798: \uc218 \ubc00\ub9ac\ucd08 \ub2e8\uc704\ub85c \uc8fc\ubb38\uc744 \ucc98\ub9ac<\/li>\n<\/ul>\n<\/div>\n<div class=\"section\">\n<h2>\ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud55c \uba38\uc2e0\ub7ec\ub2dd \uae30\ucd08<\/h2>\n<p>\ud30c\uc774\uc36c\uc740 \ub370\uc774\ud130 \uacfc\ud559\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\uc785\ub2c8\ub2e4. \uc5ec\ub7ec \uac15\ub825\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4\uc774 \uc9c0\uc6d0\ub418\uba70, \uc774\ub97c \ud1b5\ud574 \ud6a8\uc728\uc801\uc73c\ub85c \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\ub85c \uc0ac\uc6a9\ud558\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>Numpy<\/strong>: \uc218\uce58 \uacc4\uc0b0\uc5d0 \ud6a8\uc728\uc801\uc778 \ub77c\uc774\ube0c\ub7ec\ub9ac<\/li>\n<li><strong>Pandas<\/strong>: \ub370\uc774\ud130 \ucc98\ub9ac \ubc0f \ubd84\uc11d\uc744 \uc704\ud55c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430<\/li>\n<li><strong>Scikit-learn<\/strong>: \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac<\/li>\n<li><strong>TensorFlow<\/strong> \ub610\ub294 <strong>Keras<\/strong>: \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604\uc6a9 \ub77c\uc774\ube0c\ub7ec\ub9ac<\/li>\n<\/ul>\n<\/div>\n<div class=\"section\">\n<h2>\uc5ed\uc804\ud30c \uc54c\uace0\ub9ac\uc998\uc758 \uc774\ud574<\/h2>\n<p>\uc5ed\uc804\ud30c\ub294 \uc2e0\uacbd\ub9dd\uc5d0\uc11c \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8 \ud558\ub294 \ud575\uc2ec \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \uc774\ub294 \ub192\uc740 \ucc28\uc6d0\uc758 \ubb38\uc81c\uc5d0\uc11c \ud30c\ub77c\ubbf8\ud130\ub97c \ucd5c\uc801\ud654\ud558\uc5ec \ubaa8\ub378\uc758 \uc608\uce21\uc744 \uac1c\uc120\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc5ed\uc804\ud30c \uc54c\uace0\ub9ac\uc998\uc740 \ud06c\uac8c \ub2e4\uc74c\uc758 \uacfc\uc815\uc744 \ub530\ub978\ub2e4:<\/p>\n<ol>\n<li>\uc21c\uc804\ud30c(Forward Propagation): \uc785\ub825 \ub370\uc774\ud130\ub97c \uc2e0\uacbd\ub9dd\uc5d0 \ud1b5\uacfc\uc2dc\ucf1c \ucd9c\ub825\uac12\uc744 \uacc4\uc0b0.<\/li>\n<li>\uc190\uc2e4 \ud568\uc218(Loss Function) \uacc4\uc0b0: \uc608\uce21\uac12\uacfc \uc2e4\uc81c\uac12\uc758 \ucc28\uc774\ub97c \uce21\uc815.<\/li>\n<li>\uc5ed\uc804\ud30c(Backpropagation): \uc190\uc2e4\uc744 \ucd5c\uc18c\ud654\ud558\uae30 \uc704\ud574 \uac01 \uac00\uc911\uce58\uc5d0 \ub300\ud55c \uacbd\ub7c9\uc744 \uacc4\uc0b0\ud558\uace0 \uc774\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8.<\/li>\n<\/ol>\n<\/div>\n<div class=\"section\">\n<h2>\ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud55c \uc5ed\uc804\ud30c \uad6c\ud604<\/h2>\n<p>\uc774\uc81c \ud30c\uc774\uc36c\uc73c\ub85c \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud558\uace0 \uc5ed\uc804\ud30c \uc54c\uace0\ub9ac\uc998\uc744 \uc9c1\uc811 \ucf54\ub529\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uae30\ubcf8\uc801\uc778 \uc2e0\uacbd\ub9dd \uad6c\uc870\uc640 \uc21c\uc804\ud30c \ubc0f \uc5ed\uc804\ud30c \uacfc\uc815\uc744 \uad6c\ud604\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n<pre><code>\nimport numpy as np\n\nclass SimpleNeuralNetwork:\n    def __init__(self, learning_rate=0.01):\n        self.learning_rate = learning_rate\n        self.weights = np.random.rand(2, 1)  # 2\uac1c\uc758 \uc785\ub825\uc5d0 \ub300\ud55c 1\uac1c\uc758 \ucd9c\ub825\n        self.bias = np.random.rand(1)\n\n    def forward(self, X):\n        return np.dot(X, self.weights) + self.bias\n\n    def loss(self, y_hat, y):\n        return np.mean((y_hat - y) ** 2)\n\n    def backward(self, X, y, y_hat):\n        # Gradient calculation\n        d_loss = 2 * (y_hat - y) \/ y.size\n        d_weights = np.dot(X.T, d_loss)  # dL\/dW\n        d_bias = np.sum(d_loss)  # dL\/db\n        \n        # Update weights and bias\n        self.weights -= self.learning_rate * d_weights\n        self.bias -= self.learning_rate * d_bias\n\n    def train(self, X, y, epochs=1000):\n        for epoch in range(epochs):\n            y_hat = self.forward(X)\n            loss = self.loss(y_hat, y)\n            self.backward(X, y, y_hat)\n            if epoch % 100 == 0:\n                print(f'Epoch {epoch}, Loss: {loss}')\n\n# Example data\nX = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\ny = np.array([[0], [1], [1], [0]])  # XOR problem\n\n# Initialize and train the neural network\nnn = SimpleNeuralNetwork(learning_rate=0.1)\nnn.train(X, y)\n        <\/code><\/pre>\n<p>\uc704 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c XOR \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4. \ud559\uc2b5\ub960(learning rate)\uc740 \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \uc815\ub3c4\ub97c \uc870\uc808\ud569\ub2c8\ub2e4. \uc774 \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \uc6b0\ub9ac\ub294 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ucd08\uc801\uc778 \uc791\ub3d9 \uc6d0\ub9ac\ub97c \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/div>\n<div class=\"section\">\n<h2>\ubaa8\ub378 \ud3c9\uac00 \ubc0f \uac1c\uc120<\/h2>\n<p>\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4\uc5d0\ub294 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0 \ud544\uc694\uc5d0 \ub530\ub77c \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub378 \ud3c9\uac00 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\ud6c8\ub828 \uc138\ud2b8\uc640 \uac80\uc99d \uc138\ud2b8 \ub098\ub204\uae30<\/strong>: \ubaa8\ub378\uc758 \uc77c\ubc18\ud654\ub97c \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ub370\uc774\ud130\ub97c \ub098\ub215\ub2c8\ub2e4.<\/li>\n<li><strong>\uad50\ucc28 \uac80\uc99d<\/strong>: \uc5ec\ub7ec \ubc88\uc758 \ud6c8\ub828\uacfc \ud3c9\uac00\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc548\uc815\uc801\uc73c\ub85c \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815<\/strong>: \ud559\uc2b5\ub960, \ub808\uc774\uc5b4 \uac1c\uc218, \ub178\ub4dc \uac1c\uc218 \ub4f1\uc744 \uc870\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<\/div>\n<div class=\"section\">\n<h2>\uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc758 \uae30\ucd08\ub97c \ub2e4\ub904\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud30c\uc774\uc36c\uc744 \ud1b5\ud574 \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud558\uace0 \uc5ed\uc804\ud30c \uc54c\uace0\ub9ac\uc998\uc758 \uae30\ucd08 \uc6d0\ub9ac\ub97c \uc124\uba85\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ubd84\uc57c\ub294 \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uba38\uc2e0\ub7ec\ub2dd\uc758 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\ub4e4\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud558\uace0 \uc815\uad50\ud55c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\ud5a5\ud6c4\uc5d0\ub294 \ub354 \uae4a\uc774 \uc788\ub294 \uc8fc\uc81c\ub4e4, \uc608\ub97c \ub4e4\uc5b4 RNN, CNN\uacfc \uac19\uc740 \uc2ec\ud654\ub41c \ub525\ub7ec\ub2dd \uae30\ubc95\uc774\ub098 \uac15\ud654\ud559\uc2b5\uc744 \ud1b5\ud55c \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998 \uac1c\ubc1c\uc5d0 \ub300\ud574\uc11c\ub3c4 \ub2e4\ub8e8\uc5b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774\uc640 \uac19\uc740 \ub0b4\uc6a9\ub4e4\uc774 \ud000\ud2b8 \ud2b8\ub808\uc774\ub529\uc744 \uafc8\uafb8\ub294 \uc774\ub4e4\uc5d0\uac8c \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<\/div>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud604\ub300 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774 \uc810\uc810 \ub354 \ubcf4\ud3b8\ud654\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \ub2a5\ub825\uc774 \ud5a5\uc0c1\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 Python\uc744 \uc0ac\uc6a9\ud558\uace0, \uc5ed\uc804\ud30c(Backpropagation) \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubcf8 \uac1c\ub150 \uba38\uc2e0\ub7ec\ub2dd(Machine Learning)\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uace0 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd(Deep Learning)\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \ub354 \uae4a\uc774 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29421\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud574 \uc5ed\uc804\ud30c\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95&#8221;<\/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-29421","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\uba38\uc2e0\ub7ec\ub2dd 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