Add snippet for get_started index page.

samples/core/get_started/_index.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "_index.ipynb",
+      "version": "0.3.2",
+      "views": {},
+      "default_view": {},
+      "provenance": []
+    }
+  },
+  "cells": [
+    {
+      "metadata": {
+        "id": "rX8mhOLljYeM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "##### Copyright 2018 The TensorFlow Authors.\n",
+        "\n",
+        "Licensed under the Apache License, Version 2.0 (the \"License\");"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BZSlp3DAjdYf",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        },
+        "cellView": "form"
+      },
+      "cell_type": "code",
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3wF5wszaj97Y",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Get Started with TensorFlow\n",
+        "\n",
+        "First, import TensorFlow into your program:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0trJmd6DjqBZ",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7NAbSZiaoJ4z",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Load and configure the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Convert the samples from an int to a float:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7FP5258xjs-v",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist = tf.keras.datasets.mnist\n",
+        "\n",
+        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+        "\n",
+        "X_train, X_test = X_train / 255.0, X_test / 255.0"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "BPZ68wASog_I",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Build the `tf.keras` model:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rBO43XtLjhCd",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "model = tf.keras.models.Sequential([\n",
+        "  tf.keras.layers.Flatten(),\n",
+        "  tf.keras.layers.Dense(512, activation='relu'),\n",
+        "  tf.keras.layers.Dropout(0.2),\n",
+        "  tf.keras.layers.Dense(10, activation='softmax')\n",
+        "])\n",
+        "\n",
+        "model.compile(optimizer='adam', \n",
+        "              loss='sparse_categorical_crossentropy',\n",
+        "              metrics=['accuracy'])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ix4mEL65on-w",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Train and evaluate model:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cQnPHobsj0td",
+        "colab_type": "code",
+        "colab": {
+          "autoexec": {
+            "startup": false,
+            "wait_interval": 0
+          }
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "model.fit(X_train, y_train, epochs=5)\n",
+        "\n",
+        "model.evaluate(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
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