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\uae30\uc220\uc740 \ud604\ub300 \uc0ac\ud68c\uc5d0\uc11c \ud601\uc2e0\uc758 \uc911\uc694\ud55c \uc6d0\ub3d9\ub825\uc774 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uae30\uc5c5\uacfc \uae30\uad00\ub4e4\uc740 \ub370\uc774\ud130 \uae30\ubc18\uc758 \uc758\uc0ac\uacb0\uc815\uc744 \ud1b5\ud574 \ud6a8\uc728\uc131\uc744 \uadf9\ub300\ud654\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud074\ub77c\uc6b0\ub4dc \uae30\ubc18\uc758 AI \ud50c\ub7ab\ud3fc\uc740 \uc774\ub7ec\ud55c \ubcc0\ud654\ub97c \ub354\uc6b1 \uac00\uc18d\ud654\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 AWS SageMaker, Google AI Platform, Azure Machine Learning\uacfc \uac19\uc740 \uc8fc\uc694 \ud50c\ub7ab\ud3fc\uc758 \uae30\ub2a5\uacfc \uc774\uc810, \uadf8\ub9ac\uace0 \uc774\ub4e4 \ud50c\ub7ab\ud3fc\uc5d0\uc11c \ud65c\uc6a9\ud560\u2026","rel":"","context":""\uc778\uacf5\uc9c0\ub2a5\ud65c\uc6a9"\uc5d0\uc11c","block_context":{"text":"\uc778\uacf5\uc9c0\ub2a5\ud65c\uc6a9","link":"https:\/\/atmokpo.com\/w\/category\/%ec%9d%b8%ea%b3%b5%ec%a7%80%eb%8a%a5%ed%99%9c%ec%9a%a9\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":29465,"url":"https:\/\/atmokpo.com\/w\/29465\/","url_meta":{"origin":30012,"position":3},"title":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud45c\ubcf8 \uc678 \uc218\uc775\ub960\ub85c \uc804\uc9c4 \ubd84\uc11d","author":"root","date":"2024\ub144 10\uc6d4 27\uc77c","format":false,"excerpt":"\ucd5c\uadfc \uba87 \ub144 \uac04 \uc778\uacf5\uc9c0\ub2a5 \uae30\uc220\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ud2b8\ub808\uc774\ub529 \ubc29\uc2dd\uc5d0 \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \ub9ce\uc740 \ud22c\uc790\uc790\uc640 \ud000\ud2b8\ub4e4\uc774 \uc801\uc6a9\ud558\uace0 \uc788\uc73c\uba70, \uadf8 \uac00\ub2a5\uc131\uc740 \ubb34\uad81\ubb34\uc9c4\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ubd80\ud130 \uace0\uae09 \uc8fc\uc81c\uc778 \ud45c\ubcf8 \uc678 \uc218\uc775\ub960 \ubd84\uc11d(Out-of-Sample Return Analysis)\uae4c\uc9c0\ub97c \uc2ec\ub3c4 \uae4a\uac8c \ub2e4\ub8e8\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 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\ud30c\uc774\ud1a0\uce58(Pytorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud558\uace0, \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uacb0\uce21\uce58\ub97c \ud655\uc778\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub780? \ub370\uc774\ud130 \uc804\ucc98\ub9ac(Data Preprocessing)\ub294 \uc6d0\ubcf8 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uae30 \uc801\ud569\ud55c \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \uc5ec\ub7ec \ub2e8\uacc4\ub97c\u2026","rel":"","context":""\ud30c\uc774\ud1a0\uce58 \uac15\uc88c"\uc5d0\uc11c","block_context":{"text":"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c","link":"https:\/\/atmokpo.com\/w\/category\/%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":30064,"url":"https:\/\/atmokpo.com\/w\/30064\/","url_meta":{"origin":30012,"position":5},"title":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc2e4\uc2b5 \ud658\uacbd \uc124\uc815","author":"root","date":"2024\ub144 10\uc6d4 28\uc77c","format":false,"excerpt":"\ub525\ub7ec\ub2dd\uc740 \ud604\ub300 \uc778\uacf5\uc9c0\ub2a5(AI) \uae30\uc220\uc758 \ud575\uc2ec\uc73c\ub85c \uc790\ub9ac \uc7a1\uc558\uc73c\uba70, \uadf8\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ud504\ub808\uc784\uc6cc\ud06c\uac00 \ub4f1\uc7a5\ud588\uc2b5\ub2c8\ub2e4. \uadf8\uc911\uc5d0\uc11c\ub3c4 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 \ub3d9\uc801 \uc5f0\uc0b0 \ubc29\uc2dd\uacfc \uc9c1\uad00\uc801\uc778 API \ub355\ubd84\uc5d0 \ub9ce\uc740 \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc758 \uc0ac\ub791\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \ub525\ub7ec\ub2dd \uc2e4\uc2b5\uc744 \uc704\ud55c \ud658\uacbd \uc124\uc815 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud30c\uc774\ud1a0\uce58 \uc18c\uac1c \ud30c\uc774\ud1a0\uce58\ub294 Facebook AI Research Group\uc5d0 \uc758\ud574 \uac1c\ubc1c\ub41c \uc624\ud508\u2026","rel":"","context":""\ud30c\uc774\ud1a0\uce58 \uac15\uc88c"\uc5d0\uc11c","block_context":{"text":"\ud30c\uc774\ud1a0\uce58 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<\/p>\n

\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ub370 \uc788\uc5b4\uc11c \uacfc\uc801\ud569(overfitting)\uc740 \ud754\ud788 \ubc1c\uc0dd\ud558\ub294 \ubb38\uc81c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uacfc\uc801\ud569\uc740 \ubaa8\ub378\uc774 \ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \ub108\ubb34 \uacfc\ub3c4\ud558\uac8c \uc801\ud569\ub418\uba74\uc11c, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc77c\ubc18\ud654 \ub2a5\ub825\uc774 \ub5a8\uc5b4\uc9c0\ub294 \ud604\uc0c1\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \ubc29\ubc95\ub4e4\uc774 \uc874\uc7ac\ud558\uc9c0\ub9cc, \uadf8 \uc911 \ub4dc\ub86d\uc544\uc6c3(Dropout)<\/strong>\uc740 \ud2b9\ud788 \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 \ub4dc\ub86d\uc544\uc6c3\uc758 \uac1c\ub150\uacfc \uc774\ub97c \ud30c\uc774\ud1a0\uce58(PyTorch)\uc5d0\uc11c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

\ub4dc\ub86d\uc544\uc6c3(Dropout)\uc774\ub780?<\/h2>\n
\ub4dc\ub86d\uc544\uc6c3\uc740 Neural Network\uc758 \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \uc77c\ubd80 \ub274\ub7f0\uc744 \ubb34\uc791\uc704\ub85c \ube44\ud65c\uc131\ud654\ud558\uc5ec \ucd5c\uc801\ud654\ub97c \uc9c4\ud589\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc774 \ud2b9\uc815 \ub274\ub7f0\uc5d0\ub9cc \uc9c0\ub098\uce58\uac8c \uc758\uc874\ud558\uc9c0 \uc54a\ub3c4\ub85d \ud558\uc5ec, \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uace0 \ub354 \uc77c\ubc18\ud654\ub41c \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4\ub0c5\ub2c8\ub2e4. \uad6c\uccb4\uc801\uc73c\ub85c \ub4dc\ub86d\uc544\uc6c3\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud569\ub2c8\ub2e4.<\/p>\n
\n
\ud559\uc2b5 \uc911\uc5d0 \uac01 \ub274\ub7f0\uc758 \ucd9c\ub825\uac12\uc744 \ud655\ub960\uc801\uc73c\ub85c 0\uc73c\ub85c \uc124\uc815\ud569\ub2c8\ub2e4.<\/li>\n
\ub4dc\ub86d\uc544\uc6c3 \ube44\uc728(p)\uc740 \ub4dc\ub86d\uc544\uc6c3\uc744 \uc801\uc6a9\ud560 \ub274\ub7f0\uc758 \ube44\uc728\uc744 \ub098\ud0c0\ub0b4\uba70, \uc77c\ubc18\uc801\uc73c\ub85c 0.2 ~ 0.5 \uc0ac\uc774\uc758 \uac12\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n
\ubaa8\ub378\uc774 \ud3c9\uac00\ub420 \ub54c\ub294 \ubaa8\ub4e0 \ub274\ub7f0\uc744 \uc0ac\uc6a9\ud558\uace0, \ucd9c\ub825\uac12\uc740 \ub4dc\ub86d\uc544\uc6c3 \ube44\uc728\uc5d0 \ub530\ub77c \uc2a4\ucf00\uc77c\ub9c1(\uc870\uc815)\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\ub4dc\ub86d\uc544\uc6c3\uc758 \ud6a8\uacfc<\/h2>\n
\ub4dc\ub86d\uc544\uc6c3\uc744 \uc801\uc6a9\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uc7a5\uc810\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n
\uacfc\uc801\ud569 \ubc29\uc9c0:<\/strong> \ubb34\uc791\uc704\ub85c \ub274\ub7f0\uc744 \ube44\ud65c\uc131\ud654\ud568\uc73c\ub85c\uc368, \ubaa8\ub378\uc774 \ud2b9\uc815 \ud328\ud134\uc5d0 \ub9de\ucdb0 \ud559\uc2b5\ud558\ub294 \uac83\uc744 \ubc29\uc9c0\ud569\ub2c8\ub2e4.<\/li>\n
\uc568\ub9ac\uac8c\uc774\uc158(Ensemble)\uc758 \ud6a8\uacfc:<\/strong> \ub4dc\ub86d\uc544\uc6c3\uc740 \uc11c\ub85c \ub2e4\ub978 \uc11c\ube0c \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ud6a8\uacfc\ub97c \uc8fc\uc5b4 \uc559\uc0c1\ube14 \ubaa8\ub378\uacfc \ube44\uc2b7\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/li>\n
\uac04\ub2e8\ud55c \uad6c\ud604:<\/strong> \ube44\uad50\uc801 \uac04\ub2e8\ud558\uac8c \uc801\uc6a9\ud560 \uc218 \uc788\uc5b4 \ub2e4\uc591\ud55c \ubaa8\ub378\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/li>\n<\/ol>\n
\ub4dc\ub86d\uc544\uc6c3\uc744 \uc774\uc6a9\ud55c \ud30c\uc774\ud1a0\uce58 \uc608\uc81c \ucf54\ub4dc<\/h2>\n
\uc774\uc81c \ub4dc\ub86d\uc544\uc6c3\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \uc608\uc2dc\uc5d0\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790 \ubd84\ub958 \ubaa8\ub378\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4.<\/p>\n
1. \ub370\uc774\ud130\uc14b \uc900\ube44\ud558\uae30<\/h3>\n
import torch\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nfrom torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \ubcc0\ud658 \uc815\uc758\ntransform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, transform=transform, download=True)\ntest_dataset = datasets.MNIST(root='.\/data', train=False, transform=transform, download=True)\n\n# \ub370\uc774\ud130\ub85c\ub354 \uc815\uc758\ntrain_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\ntest_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)<\/code><\/pre>\n2. \ubaa8\ub378 \uc815\uc758\ud558\uae30<\/h3>\n\ub2e4\uc74c\uc73c\ub85c, \ub4dc\ub86d\uc544\uc6c3\uc744 \ud3ec\ud568\ud558\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. <\/p>\n import torch.nn as nn\nimport torch.nn.functional as F\n\nclass SimpleCNN(nn.Module):\n def __init__(self):\n super(SimpleCNN, self).__init__()\n self.conv1 = nn.Conv2d(1, 32, kernel_size=3)\n self.conv2 = nn.Conv2d(32, 64, kernel_size=3)\n self.fc1 = nn.Linear(64*6*6, 128)\n self.fc2 = nn.Linear(128, 10)\n self.dropout = nn.Dropout(p=0.5) # \ub4dc\ub86d\uc544\uc6c3 \ube44\uc728 \uc124\uc815\n\n def forward(self, x):\n x = F.relu(self.conv1(x))\n x = F.max_pool2d(x, 2)\n x = F.relu(self.conv2(x))\n x = F.max_pool2d(x, 2)\n x = x.view(x.size(0), -1) # flatten\n x = F.relu(self.fc1(x))\n x = self.dropout(x) # \ub4dc\ub86d\uc544\uc6c3 \uc801\uc6a9\n x = self.fc2(x)\n return x<\/code><\/pre>\n3. \ubaa8\ub378 \ud6c8\ub828\ud558\uae30<\/h3>\n\ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc815\uc758\ud558\uace0, \uc5d0\ud3ec\ud06c\ub97c \ubc18\ubcf5\ud558\uc5ec \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n import torch.optim as optim\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218, \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\nmodel = SimpleCNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# \ud559\uc2b5 \ud568\uc218\ndef train(model, train_loader, criterion, optimizer, epochs=5):\n model.train()\n for epoch in range(epochs):\n running_loss = 0.0\n for images, labels in train_loader:\n optimizer.zero_grad()\n outputs = model(images)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n running_loss += loss.item()\n print(f'Epoch [{epoch+1}\/{epochs}], Loss: {running_loss\/len(train_loader):.4f}')\n\n# \ubaa8\ub378 \ud6c8\ub828\ntrain(model, train_loader, criterion, optimizer, epochs=5)<\/code><\/pre>\n4. \ubaa8\ub378 \ud3c9\uac00\ud558\uae30<\/h3>\n\ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uc5ec \uc131\ub2a5\uc744 \ud655\uc778\ud569\ub2c8\ub2e4.<\/p>\n def test(model, test_loader):\n model.eval()\n correct = 0\n total = 0\n with torch.no_grad():\n for images, labels in test_loader:\n outputs = model(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n print(f'Accuracy of the model on the test images: {100 * correct \/ total:.2f}%')\n\n# \ubaa8\ub378 \ud3c9\uac00\ntest(model, test_loader)<\/code><\/pre>\n\uacb0\ub860<\/h2>\n\ub4dc\ub86d\uc544\uc6c3\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0\uc11c \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uace0 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ubcf8 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub4dc\ub86d\uc544\uc6c3\uc744 \uad6c\ud604\ud558\uace0, MNIST \ub370\uc774\ud130\uc14b \ubd84\ub958 \uc608\uc81c\ub97c \ud1b5\ud574 \uadf8 \ud6a8\uacfc\ub97c demonstrated \ud588\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uae30\ubcf8\uc801\uc778 \uc608\uc81c\uc77c \ubfd0\uc774\uba70, \uc2e4\uc81c\ub85c\ub294 \ub2e4\uc591\ud55c \uc544\ud0a4\ud14d\ucc98\uc640 \ub4dc\ub86d\uc544\uc6c3 \ube44\uc728\uc744 \uc870\uc815\ud558\uc5ec \ubcf4\ub2e4 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc124\uacc4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ucc38\uace0 \uc790\ub8cc<\/h2>\n\nPyTorch Documentation<\/a><\/li>\n MNIST Dataset<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ub370 \uc788\uc5b4\uc11c \uacfc\uc801\ud569(overfitting)\uc740 \ud754\ud788 \ubc1c\uc0dd\ud558\ub294 \ubb38\uc81c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uacfc\uc801\ud569\uc740 \ubaa8\ub378\uc774 \ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \ub108\ubb34 \uacfc\ub3c4\ud558\uac8c \uc801\ud569\ub418\uba74\uc11c, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc77c\ubc18\ud654 \ub2a5\ub825\uc774 \ub5a8\uc5b4\uc9c0\ub294 \ud604\uc0c1\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \ubc29\ubc95\ub4e4\uc774 \uc874\uc7ac\ud558\uc9c0\ub9cc, \uadf8 \uc911 \ub4dc\ub86d\uc544\uc6c3(Dropout)\uc740 \ud2b9\ud788 \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 \ub4dc\ub86d\uc544\uc6c3\uc758 \uac1c\ub150\uacfc \uc774\ub97c \ud30c\uc774\ud1a0\uce58(PyTorch)\uc5d0\uc11c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub4dc\ub86d\uc544\uc6c3(Dropout)\uc774\ub780? \ub4dc\ub86d\uc544\uc6c3\uc740 Neural Network\uc758 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub4dc\ub86d\uc544\uc6c3\uc744 \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654”<\/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":[33],"tags":[],"class_list":["post-30012","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub4dc\ub86d\uc544\uc6c3\uc744 \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654 - \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\/30012\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub4dc\ub86d\uc544\uc6c3\uc744 \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ub370 \uc788\uc5b4\uc11c \uacfc\uc801\ud569(overfitting)\uc740 \ud754\ud788 \ubc1c\uc0dd\ud558\ub294 \ubb38\uc81c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uacfc\uc801\ud569\uc740 \ubaa8\ub378\uc774 \ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \ub108\ubb34 \uacfc\ub3c4\ud558\uac8c \uc801\ud569\ub418\uba74\uc11c, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc77c\ubc18\ud654 \ub2a5\ub825\uc774 \ub5a8\uc5b4\uc9c0\ub294 \ud604\uc0c1\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \ubc29\ubc95\ub4e4\uc774 \uc874\uc7ac\ud558\uc9c0\ub9cc, \uadf8 \uc911 \ub4dc\ub86d\uc544\uc6c3(Dropout)\uc740 \ud2b9\ud788 \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 \ub4dc\ub86d\uc544\uc6c3\uc758 \uac1c\ub150\uacfc \uc774\ub97c \ud30c\uc774\ud1a0\uce58(PyTorch)\uc5d0\uc11c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub4dc\ub86d\uc544\uc6c3(Dropout)\uc774\ub780? \ub4dc\ub86d\uc544\uc6c3\uc740 Neural Network\uc758 … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub4dc\ub86d\uc544\uc6c3\uc744 \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/30012\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:19:09+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:25+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" 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