\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00 \uc2dc\uc791 - \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\/30114\/\" \/>\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, \uce90\uae00 \uc2dc\uc791 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c AI \uae30\uc220\uc774 \uae09\uc18d\ub3c4\ub85c \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub370\uc774\ud130 \uacfc\ud559 \ubd84\uc57c\uc5d0\uc11c\uc758 \ud65c\uc6a9\uc774 \ub450\ub4dc\ub7ec\uc9c0\uace0 \uc788\uc73c\uba70, \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc774 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ubc30\uc6b0\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \uc628\ub77c\uc778 \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uacf5\ubd80\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \uce90\uae00(Kaggle)\uc740 \ub370\uc774\ud130 \uacfc\ud559\uc790\uc640 \uba38\uc2e0\ub7ec\ub2dd \uc5d4\uc9c0\ub2c8\uc5b4\ub97c \uc704\ud55c \uc62c\uc778\uc6d0 \ud50c\ub7ab\ud3fc\uc73c\ub85c, \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uacfc \ubb38\uc81c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec \uce90\uae00\uc5d0\uc11c \uc2e4\uc804 \uacbd\ud5d8\uc744 \uc313\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud30c\uc774\ud1a0\uce58\ub780? … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00 \uc2dc\uc791"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/30114\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:19:39+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:49:54+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\/30114\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/30114\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00 \uc2dc\uc791\",\"datePublished\":\"2024-10-28T03:19:39+00:00\",\"dateModified\":\"2024-11-26T06:49:54+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/30114\/\"},\"wordCount\":44,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/30114\/\",\"url\":\"https:\/\/atmokpo.com\/w\/30114\/\",\"name\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00 \uc2dc\uc791 - 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{"id":30114,"date":"2024-10-28T03:19:39","date_gmt":"2024-10-28T03:19:39","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30114"},"modified":"2024-11-26T06:49:54","modified_gmt":"2024-11-26T06:49:54","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%ec%ba%90%ea%b8%80-%ec%8b%9c%ec%9e%91","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30114\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00 \uc2dc\uc791"},"content":{"rendered":"

<\/p>\n

\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c AI \uae30\uc220\uc774 \uae09\uc18d\ub3c4\ub85c \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub370\uc774\ud130 \uacfc\ud559 \ubd84\uc57c\uc5d0\uc11c\uc758 \ud65c\uc6a9\uc774 \ub450\ub4dc\ub7ec\uc9c0\uace0 \uc788\uc73c\uba70, \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc774 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ubc30\uc6b0\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \uc628\ub77c\uc778 \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uacf5\ubd80\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \uce90\uae00(Kaggle)<\/strong>\uc740 \ub370\uc774\ud130 \uacfc\ud559\uc790\uc640 \uba38\uc2e0\ub7ec\ub2dd \uc5d4\uc9c0\ub2c8\uc5b4\ub97c \uc704\ud55c \uc62c\uc778\uc6d0 \ud50c\ub7ab\ud3fc\uc73c\ub85c, \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uacfc \ubb38\uc81c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec \uce90\uae00\uc5d0\uc11c \uc2e4\uc804 \uacbd\ud5d8\uc744 \uc313\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \ud30c\uc774\ud1a0\uce58\ub780?<\/h2>\n
\ud30c\uc774\ud1a0\uce58\ub294 Facebook AI Research(FAIR)\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc624\ud508 \uc18c\uc2a4 \uba38\uc2e0\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub370 \ub9e4\uc6b0 \uc720\uc6a9\ud569\ub2c8\ub2e4. \ud2b9\ud788, \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504(Dynamic Computation Graph)\ub97c \uc9c0\uc6d0\ud558\uc5ec, \ucf54\ub4dc\uc758 \uc720\uc5f0\uc131\uacfc \uac00\ub3c5\uc131\uc774 \ub6f0\uc5b4\ub098\uba70, \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
1.1. \ud30c\uc774\ud1a0\uce58\uc758 \uc8fc\uc694 \ud2b9\uc9d5<\/h3>\n
\n
\ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504(Dynamic Computation Graph)<\/strong>: \uc2e4\ud589 \uc911\uc5d0 \uacc4\uc0b0 \uadf8\ub798\ud504\uac00 \uc0dd\uc131\ub418\ubbc0\ub85c, \ubaa8\ub378\uc758 \uad6c\uc870\ub97c \uc720\uc5f0\ud558\uac8c \ubcc0\uacbd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\ud30c\uc774\uc36cic\ud558\uac8c \uc124\uacc4\ub428<\/strong>: \ud30c\uc774\uc36c\uc758 \uae30\ubcf8 \ubb38\ubc95\uacfc \ub9e4\uc6b0 \uc720\uc0ac\ud558\uc5ec, \uc790\uc5f0\uc2a4\ub7fd\uace0 \uc9c1\uad00\uc801\uc778 \ucf54\ub4dc \uc791\uc131\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/li>\n
\uac15\ub825\ud55c GPU \uc9c0\uc6d0<\/strong>: CUDA\ub97c \ud1b5\ud574 \uac15\ub825\ud55c \ubcd1\ub82c \ucc98\ub9ac\ub97c \uc9c0\uc6d0\ud558\uba70, \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc14b\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
2. \uce90\uae00 \uc18c\uac1c<\/h2>\n
\uce90\uae00\uc740 \ub370\uc774\ud130 \uacfc\ud559 \ub300\ud68c \ud50c\ub7ab\ud3fc\uc73c\ub85c, \ucc38\uac00\uc790\ub4e4\uc740 \ub2e4\uc591\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ub370\uc774\ud130\uc14b\uc744 \ubd84\uc11d\ud558\uace0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uba70, \ucd5c\uc885\uc801\uc73c\ub85c \uc608\uce21 \uacb0\uacfc\ub97c \uc81c\ucd9c\ud558\uac8c \ub429\ub2c8\ub2e4. \uce90\uae00\uc740 \ucd08\ubcf4\uc790\ubd80\ud130 \uc804\ubb38\uac00\uae4c\uc9c0 \ubaa8\ub450 \ucc38\uc5ec\ud560 \uc218 \uc788\ub294 \uacbd\uc7c1\uc758 \uc7a5\uc774 \ub418\uba70, \ub2e4\uc591\ud55c \uc790\ub8cc\uc640 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc81c\uacf5\ud558\uc5ec \uc2e4\ub825\uc744 \uc313\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.<\/p>\n
2.1. \uce90\uae00 \uacc4\uc815 \uc0dd\uc131<\/h3>\n
\uce90\uae00\uc744 \uc2dc\uc791\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \uacc4\uc815\uc744 \uc0dd\uc131\ud574\uc57c \ud569\ub2c8\ub2e4. Kaggle \uc6f9\uc0ac\uc774\ud2b8\ub85c \uc774\ub3d9\ud558\uc5ec<\/a> \ud68c\uc6d0\uac00\uc785\uc744 \uc9c4\ud589\ud558\uc138\uc694. \uac00\uc785 \ud6c4 \ud504\ub85c\ud544\uc744 \uc124\uc815\ud558\uba74, \ub2e4\uc591\ud55c \ub300\ud68c\uc5d0 \ucc38\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3. \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uae30\ubcf8 \uc608\uc81c<\/h2>\n
\uc774\uc81c \uac04\ub2e8\ud55c \ud30c\uc774\ud1a0\uce58 \uc608\uc81c\ub97c \ud1b5\ud574 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 MNIST \uc22b\uc790 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790\ub97c \uc778\uc2dd\ud558\ub294 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
3.1. \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n
\n!pip install torch torchvision<\/code>\n <\/pre>\n
3.2. MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc740 \uc190\uae00\uc528 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uae30 \uc704\ud574 torchvision\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc\ntrainset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)<\/code>\n <\/pre>\n
3.3. \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
MLP(Multi-layer Perceptron) \uad6c\uc870\uc758 \uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub97c \ud1b5\ud574 \ubaa8\ub378\uc744 \uc815\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass SimpleNN(nn.Module):\n def __init__(self):\n super(SimpleNN, self).__init__()\n self.fc1 = nn.Linear(784, 128) # 28*28 = 784\n self.fc2 = nn.Linear(128, 10) # 10 classes for digits 0-9\n\n def forward(self, x):\n x = x.view(x.size(0), -1) # flatten input\n x = F.relu(self.fc1(x))\n x = self.fc2(x)\n return x\n\nmodel = SimpleNN()<\/code>\n <\/pre>\n
3.4. \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574, \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uae30\ubc95\uc744 \uc815\uc758\ud55c \ud6c4, \uc5ec\ub7ec \uc5d0\ud3ed(epoch) \ub3d9\uc548 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch.optim as optim\n\n# \uc190\uc2e4 \ud568\uc218\uc640 optimizer \uc815\uc758\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\n# \ubaa8\ub378 \ud559\uc2b5\nfor epoch in range(5): # 5 epochs\n running_loss = 0.0\n for images, labels in trainloader:\n optimizer.zero_grad() # gradients\ub97c 0\uc73c\ub85c \ucd08\uae30\ud654\n outputs = model(images) # Forward pass\n loss = criterion(outputs, labels) # Loss \uacc4\uc0b0\n loss.backward() # Backward pass\n optimizer.step() # Parameter \uc5c5\ub370\uc774\ud2b8\n running_loss += loss.item()\n print(f'Epoch {epoch+1}, Loss: {running_loss\/len(trainloader)}')\n<\/code>\n <\/pre>\n3.5. \ubaa8\ub378 \ud3c9\uac00<\/h3>\n\ubaa8\ub378\uc774 \uc798 \ud559\uc2b5\ub418\uc5c8\ub294\uc9c0 \ud3c9\uac00\ud558\uae30 \uc704\ud574, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \n# \ubaa8\ub378 \ud3c9\uac00\ntestset = datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n\ncorrect = 0\ntotal = 0\n\nwith torch.no_grad():\n for images, labels in testloader:\n outputs = model(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy: {100 * correct \/ total}%')<\/code>\n <\/pre>\n4. \uce90\uae00 \ub300\ud68c \ucc38\uac00\ud558\uae30<\/h2>\n\uc774\uc81c MNIST \uc608\uc81c\ub97c \ud1b5\ud574 \uae30\ubcf8\uc801\uc778 \ud30c\uc774\ud1a0\uce58 \uc0ac\uc6a9\ubc95\uc744 \uc775\ud614\uc73c\ub2c8, \uce90\uae00 \ub300\ud68c\uc5d0 \ucc38\uac00\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uce90\uae00\uc5d0\ub294 \ub2e4\uc591\ud55c \ub300\ud68c\uac00 \uc788\uc73c\uba70, \uc5ec\ub7ec\ubd84\uc758 \uad00\uc2ec \uc788\ub294 \ubd84\uc57c\uc758 \ub300\ud68c\uc5d0 \ucc38\uac00\ud558\uba74 \ub429\ub2c8\ub2e4. \uac01 \ub300\ud68c \ud398\uc774\uc9c0\uc5d0\uc11c \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc\uc640 \ud568\uaed8 \uc608\uc81c \ucf54\ub4dc\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n 4.1. \ub300\ud68c \uacfc\uc81c \uc774\ud574\ud558\uae30<\/h3>\n\ub300\ud68c\uc5d0 \ucc38\uac00\ud558\uae30 \uc804, \ubb38\uc81c \uc124\uba85\uacfc \ub370\uc774\ud130\uc14b\uc758 \uad6c\uc870\ub97c \ucda9\ubd84\ud788 \uc774\ud574\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud0c0\uc774\ud0c0\ub2c9 \uc0dd\uc874\uc790 \uc608\uce21<\/strong> \ub300\ud68c\uc5d0\uc11c\ub294 \uc2b9\uac1d\uc758 \ud2b9\uc131\uacfc \uc0dd\uc874 \uc5ec\ubd80\uc5d0 \ub300\ud55c \uc815\ubcf4\ub97c \ud65c\uc6a9\ud558\uc5ec \uc0dd\uc874\uc790 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uac8c \ub429\ub2c8\ub2e4.<\/p>\n 4.2. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub192\uc774\uae30 \uc704\ud574, \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud558\uace0, \ud544\uc694\ud55c \ud2b9\uc131\uc744 \ucd94\uac00\ud558\uba70, \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654\ud558\ub294 \ub2e8\uacc4\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n 4.3. \ubaa8\ub378 \uc120\ud0dd<\/h3>\n\ubb38\uc81c\uc758 \ud2b9\uc131\uc5d0 \ub530\ub77c \uc801\ud569\ud55c \ubaa8\ub378\uc744 \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\ub294 \uc77c\ubc18\uc801\uc73c\ub85c CNN(Convolutional Neural Network)\uc744 \uc0ac\uc6a9\ud558\uace0, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub294 RNN(Recurrent Neural Network)\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n 4.4. \uc81c\ucd9c \ubc29\uc2dd<\/h3>\n\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4, \uc608\uce21 \uacb0\uacfc\ub97c CSV \ud30c\uc77c\ub85c \uc800\uc7a5\ud558\uc5ec \uc81c\ucd9c\ud569\ub2c8\ub2e4. \ud574\ub2f9 \ud30c\uc77c\uc758 \ud615\uc2dd\uc740 \ub300\ud68c\uc5d0 \ub530\ub77c \ub2e4\ub974\ub2c8, \ubc18\ub4dc\uc2dc \uc81c\ucd9c \uaddc\uce59\uc744 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n 5. \ucee4\ubba4\ub2c8\ud2f0\uc640 \uc18c\ud1b5\ud558\uae30<\/h2>\n\uce90\uae00\uc758 \uac00\uc7a5 \ud070 \uc7a5\uc810 \uc911 \ud558\ub098\ub294 \ucee4\ubba4\ub2c8\ud2f0\uc758 \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4. \ub2e4\ub978 \ucc38\uac00\uc790\ub4e4\uc758 \ub178\ud2b8\ubd81\uc744 \ucc38\uc870\ud558\uace0, \uc9c8\ubb38\uacfc \ub2f5\ubcc0\uc744 \ud1b5\ud574 \ub9ce\uc740 \uac83\uc744 \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uacbd\ud5d8\uc774 \ub9ce\uc740 \ub370\uc774\ud130 \uacfc\ud559\uc790\uc640 \uad50\ub958\ud558\uba74 \uc131\uc7a5\ud558\ub294 \ub370 \ud070 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.<\/p>\n 5.1. \ub178\ud2b8\ubd81 \ud65c\uc6a9\ud558\uae30<\/h3>\n\uce90\uae00\uc5d0\uc11c\ub294 \uc790\uc2e0\uc758 \ucf54\ub4dc\uc640 \uacfc\uc815\uc744 \uacf5\uc720\ud560 \uc218 \uc788\ub294 \ub178\ud2b8\ubd81(NB) \uae30\ub2a5\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc790\uc2e0\uc758 \ub178\ud558\uc6b0\ub97c \uc815\ub9ac\ud558\uac70\ub098, \ub2e4\ub978 \ucc38\uac00\uc790\ub4e4\uc758 \ub178\ud558\uc6b0\ub97c \ubc30\uc6b8 \uc218 \uc788\ub294 \ud6cc\ub96d\ud55c \uc7a5\uc18c\uc785\ub2c8\ub2e4.<\/p>\n 5.2. \uc2a4\ud06c\ub9bd\ud2b8 \ubc0f Kaggle API<\/h3>\nKaggle API\ub97c \uc0ac\uc6a9\ud558\uba74 \ub370\uc774\ud130\uc14b\uc744 \uc27d\uac8c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0, \ub300\ud68c\uc5d0 \uc81c\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc790\ub3d9\ud654\ub97c \ud1b5\ud574 \ubc18\ubcf5\uc801\uc778 \uc791\uc5c5\uc744 \uac04\uc18c\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \n!kaggle competitions download -c titanic\n!kaggle kernels push<\/code>\n <\/pre>\n6. \uacb0\ub860<\/h2>\n\ub525\ub7ec\ub2dd\uc744 \uc2dc\uc791\ud558\ub294 \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc5d0\uac8c \ud30c\uc774\ud1a0\uce58\uc640 \uce90\uae00\uc740 \ud6cc\ub96d\ud55c \ucd9c\ubc1c\uc810\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc2e4\uc804 \ud504\ub85c\uc81d\ud2b8 \uacbd\ud5d8\uc744 \uc313\uace0, \ubaa8\ub378\ub9c1 \uae30\ubc95\uc744 \ubc30\uc6b0\uba70, \ucee4\ubba4\ub2c8\ud2f0\uc640 \uc18c\ud1b5\ud558\ub294 \ubc29\ubc95\uc744 \uc775\ud790 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 \ud30c\uc774\ud1a0\uce58\uc758 \uae30\ubcf8 \uc0ac\uc6a9\ubc95\uacfc \uce90\uae00 \ub300\ud68c \ucc38\uac00 \ubc29\ubc95\uc744 \uc775\ud614\ub2e4\uba74, \uc774\uc81c \ub2e4\uc591\ud55c \uc774\ub860\uacfc \uae30\uc220\uc744 \ub179\uc5ec\ub0b4\uc5b4 \uc790\uc2e0\ub9cc\uc758 \ud504\ub85c\uc81d\ud2b8\ub97c \uc2dc\uc791\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. AI\uc758 \ubbf8\ub798\ub294 \uc5ec\ub7ec\ubd84\uc758 \uc190\uc5d0 \ub2ec\ub824 \uc788\uc2b5\ub2c8\ub2e4!<\/p>\n \ubd80\ub85d<\/h2>\n \ucc38\uace0\uc790\ub8cc<\/h3>\n\n\ud30c\uc774\ud1a0\uce58 \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a><\/li>\n \uce90\uae00 \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a><\/li>\n Towards Data Science \ube14\ub85c\uadf8<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c AI \uae30\uc220\uc774 \uae09\uc18d\ub3c4\ub85c \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub370\uc774\ud130 \uacfc\ud559 \ubd84\uc57c\uc5d0\uc11c\uc758 \ud65c\uc6a9\uc774 \ub450\ub4dc\ub7ec\uc9c0\uace0 \uc788\uc73c\uba70, \ub9ce\uc740 \uc0ac\ub78c\ub4e4\uc774 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ubc30\uc6b0\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \uc628\ub77c\uc778 \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uacf5\ubd80\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \uce90\uae00(Kaggle)\uc740 \ub370\uc774\ud130 \uacfc\ud559\uc790\uc640 \uba38\uc2e0\ub7ec\ub2dd \uc5d4\uc9c0\ub2c8\uc5b4\ub97c \uc704\ud55c \uc62c\uc778\uc6d0 \ud50c\ub7ab\ud3fc\uc73c\ub85c, \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uacfc \ubb38\uc81c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec \uce90\uae00\uc5d0\uc11c \uc2e4\uc804 \uacbd\ud5d8\uc744 \uc313\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud30c\uc774\ud1a0\uce58\ub780? … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00 \uc2dc\uc791”<\/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-30114","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00 \uc2dc\uc791 - 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3.3. \ubaa8\ub378 \uad6c\ucd95<\/h3>\nMLP(Multi-layer Perceptron) \uad6c\uc870\uc758 \uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub97c \ud1b5\ud574 \ubaa8\ub378\uc744 \uc815\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\ubd80\ub85d<\/h2>\n