{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 02 최소한의 도구로 딥러닝을 시작합니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.\n",
    "\n",
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://nbviewer.org/github/rickiepark/do-it-dl/blob/master/Ch02.ipynb\"><img src=\"https://jupyter.org/assets/share.png\" width=\"60\" />주피터 노트북 뷰어로 보기</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/do-it-dl/blob/master/Ch02.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ANeYYLVfmz53"
   },
   "source": [
    "## 02-2 딥러닝을 위한 도구들을 알아봅니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 885,
     "status": "ok",
     "timestamp": 1538325361489,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "i6c2xjWYnAY6",
    "outputId": "8e3a408c-310e-4383-ef79-32d90396d92f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello list\n"
     ]
    }
   ],
   "source": [
    "my_list = [10, 'hello list', 20]\n",
    "print(my_list[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 888,
     "status": "ok",
     "timestamp": 1538325362477,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "v6MIiDoBoPXB",
    "outputId": "bcd7d44f-4477-4cdf-d859-7bcd2f19d413"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n"
     ]
    }
   ],
   "source": [
    "my_list_2 = [[10, 20, 30], [40, 50, 60]]\n",
    "print(my_list_2[1][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 905,
     "status": "ok",
     "timestamp": 1538325363484,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "ZYdIzZlUqzRQ",
    "outputId": "b9d905e5-b644-419c-ea0e-d76e519f50c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.19.1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1063,
     "status": "ok",
     "timestamp": 1538325364645,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "WyB0D8g_t7lC",
    "outputId": "01658377-4ba2-4db7-9728-8eb64b9eb44e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10 20 30]\n",
      " [40 50 60]]\n"
     ]
    }
   ],
   "source": [
    "my_arr = np.array([[10, 20, 30], [40, 50, 60]])\n",
    "print(my_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 930,
     "status": "ok",
     "timestamp": 1538325365679,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "-E8JBNIJu0v_",
    "outputId": "b8709747-0867-4b18-b194-fba0ff78dd77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 930,
     "status": "ok",
     "timestamp": 1538325366680,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "FYRVVtpCzHC_",
    "outputId": "c915a361-9bd5-4b8d-8058-7baba53f6b2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arr[0][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 639,
     "status": "ok",
     "timestamp": 1538325367447,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "0WALSGtj0V1I",
    "outputId": "75270bc3-180c-4935-a323-9a9a86adeffc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(my_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q82Hyhri1Cc5"
   },
   "source": [
    "<퀴즈>\n",
    "`my_arr` 배열의 두 번째 행의 첫 번째 원소를 `print()` 함수로 출력해 보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 934,
     "status": "ok",
     "timestamp": 1538325368477,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "eWjHmkPJ1Q_k",
    "outputId": "4347a2a5-49d1-4dab-e709-b05fd8cd1f61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40\n"
     ]
    }
   ],
   "source": [
    "print(my_arr[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uA7hgZMSfEdI"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1562,
     "status": "ok",
     "timestamp": 1538325371221,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "9a5QBQ0DhF9d",
    "outputId": "75bdd20a-3ab0-4f2d-b819-be367d1508d7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25]) # x 좌표와 y 좌표를 파이썬 리스트로 전달합니다.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1287,
     "status": "ok",
     "timestamp": 1538325372561,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "cTM7QMQPCDPg",
    "outputId": "d79836ec-af46-4c82-fb1e-f03b79c3e71c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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15S/6Yp8PSDoZEfevs1nb56yWXJ2YM9sDtvuL5T5JH5P0H2s2e1zSwWL5U5KeieLsUydzrTlOerPK5xVaKiLGI+LKiBhU+QTlMxHxW2s2a/t81ZKrE/Nl+/22L19ZlvRxSWuvXGvq67Gr/qix7UdUvjphu+3XJH1O5RM6iogvq/y9KzdJekXS/0j6nS7J9SlJv2/7vKSSpFta/Y+4sE/SbZLmiuOnknSvpJ9ela0Tc1ZLrk7M2U5JR1z+c4Dvk/SNiHjC9p9JmomIx1X+H8/f2X5F5RPXt7Q4U625/tD2zZLOF7lub0OuirpgvmrJ1Yn52iHpseJ9yTZJD0fEd2z/ntSa1yMfpQeApLIdQgEAFChwAEiKAgeApChwAEiKAgeApChwAEiKAgeApP4fssx2AUpJ+DUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([1, 2, 3, 4, 5], [1, 4, 9, 16, 25])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1649,
     "status": "ok",
     "timestamp": 1538325374278,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "1oUskZcrD0Wp",
    "outputId": "29ae4acf-2e6a-4599-fd3f-65e5d19f0683"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.randn(1000) # 표준 정규 분포를 따르는 난수 1,000개를 만듭니다.\n",
    "y = np.random.randn(1000) # 표준 정규 분포를 따르는 난수 1,000개를 만듭니다.\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "2.최소한의도구로시작합니다.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}