{"id":41620,"date":"2024-11-07T16:56:19","date_gmt":"2024-11-07T16:56:19","guid":{"rendered":"https:\/\/atmokpo.com\/w\/?p=41620"},"modified":"2024-11-26T06:36:10","modified_gmt":"2024-11-26T06:36:10","slug":"18-%ec%9d%b8%ea%b3%b5%ec%a7%80%eb%8a%a5-%ed%9b%88%eb%a0%a8%ec%9a%a9-%ec%84%9c%eb%b2%84-%ea%b5%ac%ec%b6%95%ed%95%98%ea%b8%b0-%eb%aa%a8%eb%8d%b8-%ed%9b%88%eb%a0%a8-%eb%b0%8f-%eb%8d%b0%ec%9d%b4%ed%84%b0","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/41620\/","title":{"rendered":"18.\uc778\uacf5\uc9c0\ub2a5 \ud6c8\ub828\uc6a9 \uc11c\ubc84 \uad6c\ucd95\ud558\uae30, \ubaa8\ub378 \ud6c8\ub828 \ubc0f \ub370\uc774\ud130 \uad00\ub9ac \ub300\uaddc\ubaa8 \ub370\uc774\ud130\ub97c \uc704\ud55c \uc2a4\ud1a0\ub9ac\uc9c0 \ucd5c\uc801\ud654 \ubc0f \ub370\uc774\ud130 \ud30c\uc774\ud504\ub77c\uc778 \uc124\uc815"},"content":{"rendered":"

<\/p>\n

1. \uc11c\ub860<\/h2>\n

\n \ucd5c\uadfc \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \ub370\uc774\ud130\uc758 \uc591\uc740 \uae30\ud558\uae09\uc218\uc801\uc73c\ub85c \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub294 AI \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud55c \uc911\uc694\ud55c \uc790\uc6d0\uc73c\ub85c, \ub300\uaddc\ubaa8 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\uace0 \uad00\ub9ac\ud558\ub294 \uac83\uc774 \ud544\uc218\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uc6b0\ubd84\ud22c \uc11c\ubc84\ub97c \uae30\ubc18\uc73c\ub85c \uc778\uacf5\uc9c0\ub2a5 \ud6c8\ub828\uc6a9 \uc11c\ubc84\ub97c \uad6c\ucd95\ud558\uace0, \ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud55c \ub370\uc774\ud130 \uad00\ub9ac \ubc0f \uc2a4\ud1a0\ub9ac\uc9c0 \ucd5c\uc801\ud654 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n

2. \uc778\uacf5\uc9c0\ub2a5 \ud6c8\ub828\uc6a9 \uc11c\ubc84\uc758 \ud544\uc694\uc131<\/h2>\n

\n \uc778\uacf5\uc9c0\ub2a5 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ud544\uc694\ub85c \ud558\uba70, \uc774\uc5d0 \ub530\ub77c \ub192\uc740 \ucef4\ud4e8\ud305 \uc131\ub2a5\uacfc \uc801\uc808\ud55c \uc800\uc7a5\uacf5\uac04\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \uac1c\uc778\uc6a9 \ucef4\ud4e8\ud130\ub85c\ub294 \ubaa8\ub378 \ud6c8\ub828\uc774 \uc5b4\ub824\uc6b8 \uc218 \uc788\uc73c\uba70, \uace0\uc131\ub2a5 \uc11c\ubc84\ub098 \ud074\ub77c\uc6b0\ub4dc \uc11c\ube44\uc2a4\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.\n <\/p>\n

3. \uc6b0\ubd84\ud22c \uc11c\ubc84 \uad6c\ucd95\ud558\uae30<\/h2>\n

3.1. \uc11c\ubc84 \ud658\uacbd \uc900\ube44<\/h3>\n

\n \uc6b0\uc120, \uc778\uacf5\uc9c0\ub2a5 \uc11c\ubc84\ub97c \uad6c\ucd95\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c \uc0ac\uc591\uc744 \uc120\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, NVIDIA GPU, \ucda9\ubd84\ud55c RAM, \ube60\ub978 SSD \uc2a4\ud1a0\ub9ac\uc9c0 \ub4f1\uc740 \ud6c8\ub828 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

3.2. \uc6b0\ubd84\ud22c \uc124\uce58<\/h3>\n

\n \ub2e4\uc74c \ub2e8\uacc4\ub97c \ud1b5\ud574 \uc6b0\ubd84\ud22c \uc11c\ubc84\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n <\/p>\n

1. \uc6b0\ubd84\ud22c ISO \ud30c\uc77c \ub2e4\uc6b4\ub85c\ub4dc\n2. USB \ub4dc\ub77c\uc774\ube0c\uc5d0 ISO \ud30c\uc77c \uc4f0\uae30\n3. \uc11c\ubc84\uc5d0 USB \ub4dc\ub77c\uc774\ube0c \uc5f0\uacb0 \ud6c4 \ubd80\ud305\n4. \uc124\uce58 \ub9c8\ubc95\uc0ac\uc5d0 \ub530\ub77c \uc124\uce58 \uc9c4\ud589<\/code><\/pre>\n
3.3. \ud544\uc218 \ud328\ud0a4\uc9c0 \uc124\uce58<\/h3>\n
\n        \uc6b0\ubd84\ud22c \uc11c\ubc84\uac00 \uc124\uce58\ub418\uba74 \ud544\uc694\ud55c \uc18c\ud504\ud2b8\uc6e8\uc5b4 \ubc0f \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc778\uacf5\uc9c0\ub2a5 \ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\n    <\/p>\n
sudo apt update\nsudo apt install python3-pip python3-dev\npip3 install tensorflow torch torchvision\npip3 install numpy pandas scikit-learn<\/code><\/pre>\n
4. \ub370\uc774\ud130 \uad00\ub9ac \ubc0f \uc2a4\ud1a0\ub9ac\uc9c0 \ucd5c\uc801\ud654<\/h2>\n
4.1. \ub370\uc774\ud130 \uc800\uc7a5\uc18c\uc758 \uc120\ud0dd<\/h3>\n
\n        \ub300\uaddc\ubaa8 \ub370\uc774\ud130\ub97c \uc800\uc7a5\ud558\uae30 \uc704\ud55c \ubc29\ubc95\uc73c\ub85c\ub294 \uc5ec\ub7ec \uc885\ub958\uc758 \uc2a4\ud1a0\ub9ac\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4. HDD, SSD, NAS, \ud074\ub77c\uc6b0\ub4dc \uc800\uc7a5\uc18c \ub4f1\uc774 \uc788\uc73c\uba70, \uac01\uac01\uc758 \uc7a5\ub2e8\uc810\uc744 \uace0\ub824\ud558\uc5ec \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4.\n    <\/p>\n
4.2. \ub370\uc774\ud130\ubca0\uc774\uc2a4 \uc124\uc815<\/h3>\n
\n        \uad00\uacc4\ud615 \ub370\uc774\ud130\ubca0\uc774\uc2a4(MySQL, PostgreSQL) \ub610\ub294 NoSQL \ub370\uc774\ud130\ubca0\uc774\uc2a4(MongoDB, Redis)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub300\uaddc\ubaa8 \ub370\uc774\ud130\ub97c \uad00\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub370\uc774\ud130\ubca0\uc774\uc2a4\ub294 \ub370\uc774\ud130\uc758 CRUD(Create, Read, Update, Delete) \uc791\uc5c5\uc744 \uc27d\uac8c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
sudo apt install mysql-server\nsudo mysql_secure_installation<\/code><\/pre>\n
5. \ub370\uc774\ud130 \ud30c\uc774\ud504\ub77c\uc778 \uc124\uc815<\/h2>\n
5.1. \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\n        \ub370\uc774\ud130 \uc218\uc9d1\uc740 \ubaa8\ub378 \ud6c8\ub828\uc758 \uccab \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uc6f9 \ud06c\ub864\ub7ec, API \ub610\ub294 \ub370\uc774\ud130\ubca0\uc774\uc2a4\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, Python\uc758 \uc544\ub984\ub2e4\uc6b4 \uc18c\ud06c\ub808\uc774\ud2b8(Beautiful Soup) \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc6f9 \ub370\uc774\ud130\ub97c \ud06c\ub864\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
import requests\nfrom bs4 import BeautifulSoup\n\nurl = 'https:\/\/example.com\/data'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.text, 'html.parser')\n\ndata = []\nfor item in soup.find_all('div', class_='data-item'):\n    data.append(item.text)<\/code><\/pre>\n
5.2. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\n        \uc218\uc9d1\ub41c \ub370\uc774\ud130\ub294 \ubaa8\ub378 \ud6c8\ub828\uc5d0 \uc801\ud569\ud55c \ud615\ud0dc\ub85c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub294 \uacb0\uce21\uce58 \ucc98\ub9ac, \uc815\uaddc\ud654 \ub4f1\uc758 \uc791\uc5c5\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4. Python\uc5d0\uc11c\ub294 pandas \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uc27d\uac8c \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
import pandas as pd\n\ndf = pd.read_csv('data.csv')\ndf.fillna(method='ffill', inplace=True)\ndf['normalized'] = (df['value'] - df['value'].mean()) \/ df['value'].std()<\/code><\/pre>\n
5.3. \ub370\uc774\ud130 \uc800\uc7a5 \ubc0f \uad00\ub9ac<\/h3>\n
\n        \ucc98\ub9ac\ub41c \ub370\uc774\ud130\ub294 \ud6a8\uc728\uc801\uc778 \uc800\uc7a5 \ubc0f \uad00\ub9ac\ub97c \uc704\ud574 \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc5d0 \uc800\uc7a5\ud569\ub2c8\ub2e4. SQLAlchemy\ub97c \uc0ac\uc6a9\ud558\uc5ec Python\uc5d0\uc11c \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc640\uc758 \uc5f0\uacb0\uc744 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
from sqlalchemy import create_engine\n\nengine = create_engine('mysql+pymysql:\/\/username:password@localhost\/dbname')\ndf.to_sql('table_name', con=engine, if_exists='replace', index=False)<\/code><\/pre>\n
6. \ubaa8\ub378 \ud6c8\ub828<\/h2>\n
6.1. \ud6c8\ub828 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\n        \uc900\ube44\ub41c \ub370\uc774\ud130\ub97c \ubc14\ud0d5\uc73c\ub85c \ubaa8\ub378\uc744 \ud6c8\ub828\ud569\ub2c8\ub2e4. TensorFlow \ub610\ub294 PyTorch\uc640 \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
6.2. \ubaa8\ub378 \ud6c8\ub828 \uc608\uc81c<\/h3>\n
import tensorflow as tf\n\nmodel = tf.keras.models.Sequential([\n    tf.keras.layers.Dense(128, activation='relu', input_shape=(input_dim,)),\n    tf.keras.layers.Dense(10, activation='softmax')\n])\n\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(train_data, train_labels, epochs=10)<\/code><\/pre>\n
7. \uacb0\ub860<\/h2>\n
\n        \uc778\uacf5\uc9c0\ub2a5 \ud6c8\ub828\uc744 \uc704\ud55c \uc11c\ubc84 \uad6c\ucd95\uc740 \ub370\uc774\ud130\uc758 \uc218\uc9d1, \uad00\ub9ac, \ud6c8\ub828 \uacfc\uc815\uc774 \ud6a8\uc728\uc801\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9c8 \uc218 \uc788\ub3c4\ub85d \uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \uc6b0\ubd84\ud22c \uc11c\ubc84\ub97c \uad6c\ucd95\ud558\uace0, \ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud55c \ub370\uc774\ud130 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc124\uc815\ud558\uc5ec \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ucd5c\uc801\ud654\ub97c \uc2e4\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc778\uacf5\uc9c0\ub2a5 \uae30\uc220\uc774 \ubc1c\uc804\ud558\uba74\uc11c \ub370\uc774\ud130 \uad00\ub9ac\uc758 \uc911\uc694\uc131\uc774 \ub354\uc6b1 \ucee4\uc9c8 \uac83\uc774\uba70, \ud6a8\uc728\uc801\uc778 \uc11c\ubc84 \uad6c\uc131\uc740 \uc774\ub7ec\ud55c \ubcc0\ud654\uc5d0 \ub300\uc751\ud558\uae30 \uc704\ud55c \ud544\uc218 \uc694\uc18c\uac00 \ub420 \uac83\uc785\ub2c8\ub2e4.\n    <\/p>\n
8. \ucc38\uace0 \uc790\ub8cc<\/h2>\n