{"id":40414,"date":"2024-11-04T01:51:33","date_gmt":"2024-11-04T01:51:33","guid":{"rendered":"https:\/\/atmokpo.com\/w\/?p=40414"},"modified":"2024-11-26T06:39:14","modified_gmt":"2024-11-26T06:39:14","slug":"21-%ed%95%a8%ec%88%98-%ea%b7%bc%ec%82%ac%eb%a5%bc-%ed%86%b5%ed%95%9c-%eb%8c%80%ea%b7%9c%eb%aa%a8-%ec%83%81%ed%83%9c-%ea%b3%b5%ea%b0%84-%ec%b2%98%eb%a6%ac-%ec%8b%a0%ea%b2%bd%eb%a7%9d%ec%9d%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/40414\/","title":{"rendered":"21. \ud568\uc218 \uadfc\uc0ac\ub97c \ud1b5\ud55c \ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04 \ucc98\ub9ac, \uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud55c Q-\ud568\uc218 \ubc0f \uc815\ucc45 \uadfc\uc0ac"},"content":{"rendered":"<p>\uac15\ud654\ud559\uc2b5(Reinforcement Learning, RL)\uc740 \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ucd5c\uc801\uc758 \ud589\ub3d9 \uc815\ucc45\uc744 \ud559\uc2b5\ud574 \ub098\uac00\ub294 \uacfc\uc815\uc744 \ub2e4\ub8ec\ub2e4. \ud558\uc9c0\ub9cc, \ub9ce\uc740 \uc2e4\uc81c \ubb38\uc81c\ub4e4\uc740 \ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04\uc744 \ucc98\ub9ac\ud574\uc57c \ud558\uba70, \uc774\ub85c \uc778\ud574 \uc804\ud1b5\uc801\uc778 \uac15\ud654\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc758 \uc801\uc6a9\uc774 \uc5b4\ub835\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ud568\uc218 \uadfc\uc0ac(Function Approximation) \uae30\ubc95\uc774 \ub3c4\uc785\ub418\uc5c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud55c Q-\ud568\uc218 \ubc0f \uc815\ucc45 \uadfc\uc0ac\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\ub2e4.<\/p>\n<h2>1. \ud568\uc218 \uadfc\uc0ac\ub780?<\/h2>\n<p>\ud568\uc218 \uadfc\uc0ac\ub294 \ube44\uc120\ud615 \ub610\ub294 \uace0\ucc28\uc6d0 \ud568\uc218\uc758 \uadfc\uc0ac\ub97c \ud1b5\ud574 \uc0c1\ud0dc \ub610\ub294 \ud589\ub3d9\uc758 \uac00\uce58\ub97c \ucd94\uc815\ud558\ub294 \uae30\ubc95\uc774\ub2e4. \uac15\ud654\ud559\uc2b5\uc758 \ub9e5\ub77d\uc5d0\uc11c, \uc5d0\uc774\uc804\ud2b8\ub294 \uc0c1\ud0dc\uc640 \ud589\ub3d9\uc758 \uac00\uce58\ub97c \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud574\ub2f9 \ud568\uc218\uc758 \uadfc\uc0ac\ub97c \ud544\uc694\ub85c \ud55c\ub2e4. \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c \uc88b\uc740 \ud589\ub3d9\uc744 \uc120\ud0dd\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud574\ub2f9 \uc0c1\ud0dc\uc758 \uac00\uce58\ub97c \uc608\uce21\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \ucd5c\uc801 \uc815\ucc45(optimal policy)\uc744 \ub3c4\ucd9c\ud574\uc57c \ud55c\ub2e4.<\/p>\n<h3>1.1. Q-\ud568\uc218 \uadfc\uc0ac<\/h3>\n<p>Q-\ud568\uc218\ub294 \uc8fc\uc5b4\uc9c4 \uc0c1\ud0dc\uc640 \ud589\ub3d9\uc5d0 \ub300\ud55c \uac00\uce58\ub97c \ub098\ud0c0\ub0b4\uba70, \uc5d0\uc774\uc804\ud2b8\uac00 \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c \ud2b9\uc815 \ud589\ub3d9\uc744 \uc120\ud0dd\ud588\uc744 \ub54c, \uc55e\uc73c\ub85c \ubc1b\uc744 \ubcf4\uc0c1\uc758 \uae30\ub300\uac12\uc744 \uacc4\uc0b0\ud55c\ub2e4. \uace0\ucc28\uc6d0 \uc0c1\ud0dc \uacf5\uac04\uc5d0\uc11c\ub294 Q-\ud568\uc218\ub97c \uc815\ud655\ud558\uac8c \uc800\uc7a5\ud558\uae30 \ud798\ub4e4\uae30 \ub54c\ubb38\uc5d0 \ud568\uc218 \uadfc\uc0ac\ub97c \uc0ac\uc6a9\ud558\uc5ec Q-\ud568\uc218\ub97c \uadfc\uc0ac\ud560 \uc218 \uc788\ub2e4.<\/p>\n<h4>\uc608\uc81c: Q-\ud568\uc218 \uadfc\uc0ac<\/h4>\n<p>\ub2e4\uc74c\uc740 \uac04\ub2e8\ud55c Q-\ud568\uc218 \uadfc\uc0ac\ub97c \uad6c\ud604\ud55c \uc608\uc81c\uc774\ub2e4:<\/p>\n<pre><code>\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom collections import deque\nimport random\n\nclass DQNAgent:\n    def __init__(self, state_size, action_size):\n        self.state_size = state_size\n        self.action_size = action_size\n        self.memory = deque(maxlen=2000)\n        self.gamma = 0.95    # \ud560\uc778 \uc778\uc790\n        self.epsilon = 1.0    # \ud0d0\uc0ac \ud655\ub960\n        self.epsilon_min = 0.01\n        self.epsilon_decay = 0.995\n        self.model = self._build_model()\n        \n    def _build_model(self):\n        # \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uc815\uc758\n        model = keras.Sequential()\n        model.add(keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))\n        model.add(keras.layers.Dense(24, activation='relu'))\n        model.add(keras.layers.Dense(self.action_size, activation='linear'))\n        model.compile(loss='mse', optimizer=keras.optimizers.Adam(lr=0.001))\n        return model\n    \n    def remember(self, state, action, reward, next_state, done):\n        self.memory.append((state, action, reward, next_state, done))\n\n    def act(self, state):\n        if np.random.rand() &lt;= self.epsilon:\n            return random.randrange(self.action_size)  # \ud0d0\uc0ac\n        act_values = self.model.predict(state)  # Q-\uac12 \uc608\uce21\n        return np.argmax(act_values[0])  # \ucd5c\uc801 \ud589\ub3d9 \ubc18\ud658\n\n    def replay(self, batch_size):\n        minibatch = random.sample(self.memory, batch_size)\n        for state, action, reward, next_state, done in minibatch:\n            target = reward\n            if not done:\n                target += self.gamma * np.amax(self.model.predict(next_state)[0])  # \ub2e4\uc74c \uc0c1\ud0dc\uc758 \ucd5c\ub300 Q-\uac12\n            target_f = self.model.predict(state)\n            target_f[0][action] = target\n            self.model.fit(state, target_f, epochs=1, verbose=0)\n        if self.epsilon &gt; self.epsilon_min:\n            self.epsilon *= self.epsilon_decay\n\n# \ud658\uacbd \ubc0f \uc5d0\uc774\uc804\ud2b8 \uc124\uc815 (\uc608: OpenAI Gym)\n# ...\n<\/code><\/pre>\n<h3>1.2. \uc815\ucc45 \uadfc\uc0ac<\/h3>\n<p>\uc815\ucc45 \uadfc\uc0ac\ub294 \uc5d0\uc774\uc804\ud2b8\uac00 \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c \uc5b4\ub5a4 \ud589\ub3d9\uc744 \uc120\ud0dd\ud560\uc9c0\ub97c \uacb0\uc815\ud558\ub294 \ud568\uc218\ub85c, \uc77c\ubc18\uc801\uc73c\ub85c \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\ud604\ub420 \uc218 \uc788\ub2e4. \uc815\ucc45\uc774 \uc9c1\uc811\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ub418\ubbc0\ub85c, Q-\uac12\uc744 \uc0ac\uc6a9\ud558\ub294 \uba54\uc2dc\ub2f9 \ubc29\ubc95\ubcf4\ub2e4 \ub354 \uc790\uc5f0\uc2a4\ub7fd\uace0 \ud6a8\uc728\uc801\uc73c\ub85c \ucd5c\uc801 \uc815\ucc45\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub2e4.<\/p>\n<h4>\uc608\uc81c: \uc815\ucc45 \uadfc\uc0ac<\/h4>\n<p>\ub2e4\uc74c\uc740 \uc815\ucc45 \uadfc\uc0ac\ub97c \uad6c\ud604\ud55c \uac04\ub2e8\ud55c \uacbd\uc6b0\uc774\ub2e4:<\/p>\n<pre><code>\nclass PolicyAgent:\n    def __init__(self, state_size, action_size):\n        self.state_size = state_size\n        self.action_size = action_size\n        self.model = self._build_model()\n        \n    def _build_model(self):\n        # \uc815\ucc45 \uc2e0\uacbd\ub9dd \uc815\uc758\n        model = keras.Sequential()\n        model.add(keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))\n        model.add(keras.layers.Dense(24, activation='relu'))\n        model.add(keras.layers.Dense(self.action_size, activation='softmax'))  # \ud655\ub960\uc801 \ud589\ub3d9 \uc120\ud0dd\n        model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(lr=0.001))\n        return model\n\n    def act(self, state):\n        action_probabilities = self.model.predict(state)  # \uc0c1\ud0dc\uc5d0 \ub300\ud55c \ud589\ub3d9 \ud655\ub960 \uc608\uce21\n        return np.random.choice(self.action_size, p=action_probabilities[0])  # \ud655\ub960\uc801\uc73c\ub85c \ud589\ub3d9 \uc120\ud0dd\n\n    def train(self, states, actions, target):\n        self.model.fit(states, target, epochs=1, verbose=0)\n\n# \uc815\ucc45 \uc5d0\uc774\uc804\ud2b8 \uc0ac\uc6a9 \uc608\n# ...\n<\/code><\/pre>\n<h2>2. \ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04 \ubb38\uc81c<\/h2>\n<p>\ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04 \ucc98\ub9ac\uc5d0\uc11c\uc758 \uc8fc\uc694 \uacfc\uc81c\ub294 &#8216;\uce58\uc218 \uc800\uc8fc(curse of dimensionality)&#8217;\uc774\uba70, \uc774\ub294 \uac01 \uc0c1\ud0dc\uc640 \ud589\ub3d9 \uc870\ud569\uc5d0 \ub300\ud55c Q-\uac12\uc774\ub098 \uc815\ucc45\uc744 \uc9c1\uc811\uc801\uc73c\ub85c \uc800\uc7a5\ud558\ub294 \uac83\uc774 \ubd88\uac00\ub2a5\ud558\uac8c \ub9cc\ub4e0\ub2e4. \ud568\uc218 \uadfc\uc0ac \uae30\ubc95\uc740 \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc774\ub2e4.<\/p>\n<p>\uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud55c \ud568\uc218 \uadfc\uc0ac\ub294 \ub2e4\uc591\ud55c \uc774\uc810\uc774 \uc788\ub2e4. \uccab\uc9f8, \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \ucd5c\uc801\ud654\uac00 \uac00\ub2a5\ud558\ub2e4. \ub458\uc9f8, \ube44\uc120\ud615 \uad00\uacc4\ub97c \uc790\uc5f0\uc2a4\ub7fd\uac8c \ubaa8\ub378\ub9c1 \ud560 \uc218 \uc788\ub2e4. \ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uc815\ucc45\uc774\ub098 \uac00\uce58 \ud568\uc218\uc758 \uc77c\ubc18\ud654\uac00 \uac00\ub2a5\ud558\uba70, \uc774\ub294 \uc0c8\ub85c\uc6b4 \uc0c1\ud0dc\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \ud5a5\uc0c1\uc2dc\ud0a8\ub2e4.<\/p>\n<h3>2.1. \uc2e0\uacbd\ub9dd\uc758 \uad6c\uc870<\/h3>\n<p>\uc2e0\uacbd\ub9dd\uc758 \uad6c\uc870\ub294 \ubb38\uc81c\uc5d0 \ub530\ub77c \ub2e4\ub974\uac8c \uc124\uc815\ub420 \uc218 \uc788\uc73c\uba70, \uc77c\ubc18\uc801\uc73c\ub85c \uc785\ub825\uce35\uc5d0\ub294 \uc0c1\ud0dc \ubca1\ud130\uac00 \ub4e4\uc5b4\uac00\uace0, \ucd9c\ub825\uce35\uc5d0\ub294 \ud589\ub3d9\uc758 \uac12(Q-\ud568\uc218\uc778 \uacbd\uc6b0) \ub610\ub294 \ud589\ub3d9\uc758 \ud655\ub960(\uc815\ucc45\uc778 \uacbd\uc6b0)\uc774 \ucd9c\ub825\ub41c\ub2e4. \uc911\uac04\uce35\uc758 \uc218\uc640 \ub274\ub7f0\uc758 \uc218\ub294 \ubb38\uc81c\uc758 \ubcf5\uc7a1\ub3c4\uc5d0 \ub530\ub77c \uc870\uc808\ub418\uc5b4\uc57c \ud55c\ub2e4.<\/p>\n<h3>2.2. \uacbd\ud5d8 \uc7ac\uc0dd(Experience Replay)<\/h3>\n<p>\uacbd\ud5d8 \uc7ac\uc0dd\uc740 \uc5d0\uc774\uc804\ud2b8\uac00 \uacfc\uac70\uc758 \uacbd\ud5d8\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc73c\ub85c, \uc774 \uae30\ubc95\uc740 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud560 \ub54c \ub354\uc6b1 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud55c\ub2e4. \uacbd\ud5d8\uc744 \uc800\uc7a5\ud558\uace0 \ub79c\ub364 \uc0d8\ud50c\ub9c1\ud558\uc5ec \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc740 \ub370\uc774\ud130\uc758 \uc0c1\uad00\uc131\uc744 \uc904\uc774\uace0, \ud559\uc2b5 \uc548\uc815\uc131\uc744 \uc99d\uac00\uc2dc\ud0a8\ub2e4.<\/p>\n<h2>3. \uacb0\ub860<\/h2>\n<p>\ud568\uc218 \uadfc\uc0ac\ub97c \ud1b5\ud55c Q-\ud568\uc218 \ubc0f \uc815\ucc45 \uadfc\uc0ac\ub294 \ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04\uc5d0\uc11c \ud6a8\uacfc\uc801\uc73c\ub85c \uc791\ub3d9\ud558\ub294 \uac15\ud654\ud559\uc2b5\uc758 \ud575\uc2ec \uc694\uc18c\uc774\ub2e4. \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ube44\uc120\ud615 \ud568\uc218 \uadfc\uc0ac\ub97c \uc2e4\ud589\ud568\uc73c\ub85c\uc368 \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ud658\uacbd\uc5d0\uc11c\ub3c4 \uc5d0\uc774\uc804\ud2b8\uac00 \uc131\uacf5\uc801\uc73c\ub85c \ub3d9\uc791\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\ub294\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c \uc124\uba85\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \ud658\uacbd\uc5d0\uc11c \uac15\ud654\ud559\uc2b5\uc758 \uae30\ucd08\ub97c \ub2e4\uc838\ubcf4\uc2dc\uae30 \ubc14\ub780\ub2e4.<\/p>\n<h2>4. \ucc38\uace0 \ubb38\ud5cc<\/h2>\n<ul>\n<li>Sutton, R. S., &amp; Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.<\/li>\n<li>Silver, D., &amp; Odena, A. (2016). Neural Network-based function approximation in reinforcement learning. <\/li>\n<li>OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\uac15\ud654\ud559\uc2b5(Reinforcement Learning, RL)\uc740 \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ucd5c\uc801\uc758 \ud589\ub3d9 \uc815\ucc45\uc744 \ud559\uc2b5\ud574 \ub098\uac00\ub294 \uacfc\uc815\uc744 \ub2e4\ub8ec\ub2e4. \ud558\uc9c0\ub9cc, \ub9ce\uc740 \uc2e4\uc81c \ubb38\uc81c\ub4e4\uc740 \ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04\uc744 \ucc98\ub9ac\ud574\uc57c \ud558\uba70, \uc774\ub85c \uc778\ud574 \uc804\ud1b5\uc801\uc778 \uac15\ud654\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc758 \uc801\uc6a9\uc774 \uc5b4\ub835\ub2e4. \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \ud568\uc218 \uadfc\uc0ac(Function Approximation) \uae30\ubc95\uc774 \ub3c4\uc785\ub418\uc5c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud55c Q-\ud568\uc218 \ubc0f \uc815\ucc45 \uadfc\uc0ac\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\ub2e4. 1. \ud568\uc218 \uadfc\uc0ac\ub780? \ud568\uc218 \uadfc\uc0ac\ub294 \ube44\uc120\ud615 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/40414\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;21. \ud568\uc218 \uadfc\uc0ac\ub97c \ud1b5\ud55c \ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04 \ucc98\ub9ac, \uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud55c Q-\ud568\uc218 \ubc0f \uc815\ucc45 \uadfc\uc0ac&#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":[213],"tags":[],"class_list":["post-40414","post","type-post","status-publish","format-standard","hentry","category-213"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>21. \ud568\uc218 \uadfc\uc0ac\ub97c \ud1b5\ud55c \ub300\uaddc\ubaa8 \uc0c1\ud0dc \uacf5\uac04 \ucc98\ub9ac, \uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud55c Q-\ud568\uc218 \ubc0f \uc815\ucc45 \uadfc\uc0ac - \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\/40414\/\" \/>\n<meta property=\"og:locale\" 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\uc5d0\uc774\uc804\ud2b8\uac00\u2026","rel":"","context":"&quot;\uac15\ud654\ud559\uc2b5&quot;\uc5d0\uc11c","block_context":{"text":"\uac15\ud654\ud559\uc2b5","link":"https:\/\/atmokpo.com\/w\/category\/%ea%b0%95%ed%99%94%ed%95%99%ec%8a%b5\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":40452,"url":"https:\/\/atmokpo.com\/w\/40452\/","url_meta":{"origin":40414,"position":2},"title":"4. \uac15\ud654\ud559\uc2b5\uacfc \ub2e4\ub978 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc758 \ube44\uad50, \uac15\ud654\ud559\uc2b5\uc758 \uc7a5\uc810\uacfc \ub2e8\uc810","author":"root","date":"2024\ub144 11\uc6d4 04\uc77c","format":false,"excerpt":"\uc791\uc131\uc77c: 2023\ub144 10\uc6d4 \uc791\uc131\uc790: \uc870\uad11\ud615 1. \uc11c\ub860 \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uace0 \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc608\uce21 \ud639\uc740 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \uae30\uc220\uc774\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 \uac15\ud654\ud559\uc2b5\uc740 \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ubcf4\uc0c1 \uc2e0\ud638\ub97c \ud1b5\ud574 \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c, \ub2e4\ub978 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\ub4e4\uacfc\ub294 \ud655\uc5f0\ud788 \ub2e4\ub978 \ud2b9\uc9d5\uc744 \uac00\uc9c4\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uac15\ud654\ud559\uc2b5\uacfc \ub2e4\ub978 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\ub4e4\uc744 \ube44\uad50\ud558\uace0, \uac15\ud654\ud559\uc2b5\uc758 \uc7a5\uc810\uacfc \ub2e8\uc810\uc744 \uc2ec\ub3c4 \uc788\uac8c\u2026","rel":"","context":"&quot;\uac15\ud654\ud559\uc2b5&quot;\uc5d0\uc11c","block_context":{"text":"\uac15\ud654\ud559\uc2b5","link":"https:\/\/atmokpo.com\/w\/category\/%ea%b0%95%ed%99%94%ed%95%99%ec%8a%b5\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":40512,"url":"https:\/\/atmokpo.com\/w\/40512\/","url_meta":{"origin":40414,"position":3},"title":"54. \ubbf8\ub798\uc758 \uac15\ud654\ud559\uc2b5  \ucd5c\uc2e0 \uc5f0\uad6c \ub3d9\ud5a5\uacfc \ud5a5\ud6c4 \uc804\ub9dd, \uac15\ud654\ud559\uc2b5\uc758 \ubbf8\ub798 \uac00\ub2a5\uc131\uacfc \uc751\uc6a9 \ubd84\uc57c","author":"root","date":"2024\ub144 11\uc6d4 04\uc77c","format":false,"excerpt":"\uac15\ud654\ud559\uc2b5\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \uac00\uc7a5 \ud765\ubbf8\ub85c\uc6b4 \uc601\uc5ed \uc911 \ud558\ub098\ub85c, \uae30\uacc4 \ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c\uc368, \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ubc30\uc6b0\ub294 \uacfc\uc815\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uac15\ud654\ud559\uc2b5\uc758 \ucd5c\uc2e0 \uc5f0\uad6c \ub3d9\ud5a5\uacfc \uc804\ub9dd, \uadf8\ub9ac\uace0 \uc774 \uae30\uc220\uc774 \uc801\uc6a9\ub420 \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \ubd84\uc57c\ub97c \uc2ec\uce35\uc801\uc73c\ub85c \ud0d0\uad6c\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uac15\ud654\ud559\uc2b5\uc758 \uac1c\uc694 \uac15\ud654\ud559\uc2b5\uc740 \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uc5d0\uc11c\uc758 \uacbd\ud5d8\uc744 \ud1b5\ud574 \ubcf4\uc0c1\uc744 \ucd5c\ub300\ud654\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.\u2026","rel":"","context":"&quot;\uac15\ud654\ud559\uc2b5&quot;\uc5d0\uc11c","block_context":{"text":"\uac15\ud654\ud559\uc2b5","link":"https:\/\/atmokpo.com\/w\/category\/%ea%b0%95%ed%99%94%ed%95%99%ec%8a%b5\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":38958,"url":"https:\/\/atmokpo.com\/w\/38958\/","url_meta":{"origin":40414,"position":4},"title":"\ub9ac\uc561\ud2b8 \ud504\ub860\ud2b8\uc564\ub4dc","author":"root","date":"2024\ub144 11\uc6d4 02\uc77c","format":false,"excerpt":"\ub9ac\uc561\ud2b8\ub294 Facebook\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc624\ud508 \uc18c\uc2a4 \uc790\ubc14\uc2a4\ud06c\ub9bd\ud2b8 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \uc0ac\uc6a9\uc790 \uc778\ud130\ud398\uc774\uc2a4(UI)\ub97c \uad6c\ucd95\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ub9ac\uc561\ud2b8\ub97c \ud65c\uc6a9\ud558\uc5ec \ud504\ub860\ud2b8\uc5d4\ub4dc \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc744 \uac1c\ubc1c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uc788\uac8c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \ub9ac\uc561\ud2b8\uc758 \ucd5c\uc2e0 \uae30\ub2a5\uacfc \ubaa8\ubc94 \uc0ac\ub840\ub97c \uc81c\uacf5\ud558\uace0 \uc2e4\uc81c \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub9ac\uc561\ud2b8\uc758 \uae30\ubcf8 \uac1c\ub150 \ub9ac\uc561\ud2b8\ub294 \ucef4\ud3ec\ub10c\ud2b8 \uae30\ubc18 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \ucef4\ud3ec\ub10c\ud2b8\ub294 UI\uc758 \ub3c5\ub9bd\uc801\uc778 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\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\ub294 \ube44\uc57d\uc801\uc778 \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc73c\uba70, \uadf8 \uc911\uc5d0\uc11c\ub3c4 \uc21c\ud658 \uc2e0\uacbd\ub9dd(Recurrent Neural Network, RNN)\uc740 \uc5b8\uc5b4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc778 \ubaa8\ub378\ub85c \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 RNN\uc758 \uc6d0\ub9ac, \uad6c\uc870, \uadf8\ub9ac\uace0\u2026","rel":"","context":"&quot;\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac&quot;\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd 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