Hello,_Colaboratory.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hello, Colaboratory",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/tnmurthy/Data-Science/blob/master/Hello,_Colaboratory.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "9J7p406abzgl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<img height=\"60px\" src=\"https://colab.research.google.com/img/colab_favicon.ico\" align=\"left\" hspace=\"20px\" vspace=\"5px\">\n",
        "\n",
        "<h1>Welcome to Colaboratory!</h1>\n",
        "Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. See our [FAQ](https://research.google.com/colaboratory/faq.html) for more info."
      ]
    },
    {
      "metadata": {
        "id": "-Rh3-Vt9Nev9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Getting Started\n",
        "- [Overview of Colaboratory](/notebooks/basic_features_overview.ipynb)\n",
        "- [Loading and saving data: Local files, Drive, Sheets, Google Cloud Storage](/notebooks/io.ipynb)\n",
        "- [Importing libraries and installing dependencies](/notebooks/snippets/importing_libraries.ipynb)\n",
        "- [Using Google Cloud BigQuery](/notebooks/bigquery.ipynb)\n",
        "- [Forms](/notebooks/forms.ipynb), [Charts](/notebooks/charts.ipynb), [Markdown](/notebooks/markdown_guide.ipynb), & [Widgets](/notebooks/widgets.ipynb)\n",
        "- [TensorFlow with GPU](/notebooks/gpu.ipynb)\n",
        "- [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/): [Intro to Pandas](/notebooks/mlcc/intro_to_pandas.ipynb) & [First Steps with TensorFlow](/notebooks/mlcc/first_steps_with_tensor_flow.ipynb)\n"
      ]
    },
    {
      "metadata": {
        "id": "1fr51oVCHRZU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Highlighted Features\n",
        "### Seedbank\n",
        "\n",
        "Looking for Colab notebooks to learn from? Check out [Seedbank](https://tools.google.com/seedbank/), a place to discover interactive machine learning examples."
      ]
    },
    {
      "metadata": {
        "id": "9wi5kfGdhK0R",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### TensorFlow execution"
      ]
    },
    {
      "metadata": {
        "id": "S9GW-n-oYWIj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Colaboratory allows you to execute TensorFlow code in your browser with a single click. The example below adds two matrices.\n",
        "\n",
        "$\\begin{bmatrix}\n",
        "  1. & 1. & 1. \\\\\n",
        "  1. & 1. & 1. \\\\\n",
        "\\end{bmatrix} +\n",
        "\\begin{bmatrix}\n",
        "  1. & 2. & 3. \\\\\n",
        "  4. & 5. & 6. \\\\\n",
        "\\end{bmatrix} =\n",
        "\\begin{bmatrix}\n",
        "  2. & 3. & 4. \\\\\n",
        "  5. & 6. & 7. \\\\\n",
        "\\end{bmatrix}$"
      ]
    },
    {
      "metadata": {
        "id": "oYZkU7ZN3CL0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "9589151d-5ff4-4bca-f05e-8bcba8ffcba1"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "input1 = tf.ones((2, 3))\n",
        "input2 = tf.reshape(tf.range(1, 7, dtype=tf.float32), (2, 3))\n",
        "output = input1 + input2\n",
        "\n",
        "with tf.Session():\n",
        "  result = output.eval()\n",
        "result  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2., 3., 4.],\n",
              "       [5., 6., 7.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "metadata": {
        "id": "nwYF0E3Sjiy4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### GitHub\n",
        "\n",
        "You can save a copy of your Colab notebook to Github by using File > Save a copy to GitHub…\n",
        "\n",
        "You can load any .ipynb on GitHub by just adding the path to colab.research.google.com/github/ . For example, [colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb) will load [this .ipynb](https://github.com/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb) on GitHub.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "yv2XIwi5hQ_g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Visualization"
      ]
    },
    {
      "metadata": {
        "id": "rYs5mx2JZkmy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Colaboratory includes widely used libraries like [matplotlib](https://matplotlib.org/), simplifying visualization."
      ]
    },
    {
      "metadata": {
        "id": "xqrc5C-IaA5J",
        "colab_type": "code",
        "colab": {
          "height": 360
        },
        "outputId": "3460cc84-faf8-4d8c-a4e6-96a6809c389a"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.arange(20)\n",
        "y = [x_i + np.random.randn(1) for x_i in x]\n",
        "a, b = np.polyfit(x, y, 1)\n",
        "_ = plt.plot(x, y, 'o', np.arange(20), a*np.arange(20)+b, '-')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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pbHNv4qHMpRqZLSIRSQUtpgoEA/zP99/ive5TGM4RbIFoHk15mm8/sh6nRmaL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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x562d64cf1b10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "AN_LRQ9NkOjs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Want to use a new library?  `pip install` it at the top of the notebook. Then that library can be used anywhere else in the notebook. For recipes to import commonly used libraries, refer to the [importing libraries example notebook](/notebooks/snippets/importing_libraries.ipynb)."
      ]
    },
    {
      "metadata": {
        "id": "FlQq0SUepQbd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "6ecc80ce-4af1-47a2-d0d1-9799cf0d5afd"
      },
      "cell_type": "code",
      "source": [
        "!pip install -q matplotlib-venn\n",
        "\n",
        "from matplotlib_venn import venn2\n",
        "_ = venn2(subsets = (3, 2, 4))"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c9uJpSTIVF52+fjdIAcEplVE67iAaIT85CUxPi65CCV7MRMlhL56Wbk1dz5afDx8HL0Rn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\nMeSJiBTGkCciUhhDnohIYQx5IiKF/T/d7ovGTtMQygAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f7ec2c396a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LxZ3dPzYnyNF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Forms\n",
        "\n",
        "Forms can be used to parameterize code. See the [forms example notebook](/notebooks/forms.ipynb) for more details."
      ]
    },
    {
      "metadata": {
        "id": "FQ_Hx_9tn7uF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Examples\n",
        "\n",
        "text = 'value' #@param \n",
        "date_input = '2018-03-22' #@param {type:\"date\"}\n",
        "number_slider = -0.4 #@param {type:\"slider\", min:-1, max:1, step:0.1}\n",
        "dropdown = '1st option' #@param [\"1st option\", \"2nd option\", \"3rd option\"]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rTX3heEtu0b2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Local runtime support\n",
        "\n",
        "Colab  supports connecting to a Jupyter runtime on your local machine. For more information, see our [documentation](https://research.google.com/colaboratory/local-runtimes.html)."
      ]
    },
    {
      "metadata": {
        "id": "bdSaZJguVIPV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ]
    }
  ]
}