"Note: you can run **[this notebook, live in Google Colab](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/eager.ipynb)** with zero setup.\n",
" <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a> \n",
"</td><td>\n",
"<a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/samples/core/get_started/eager.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on Github</a></td></table>\n",
"\n"
]
},
{
"metadata": {
"id": "LDrzLFXE8T1l",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"This tutorial describes how to use machine learning to *categorize* Iris flowers by species. It uses [TensorFlow](https://www.tensorflow.org)'s eager execution to (1) build a *model*, (2) *train* the model on example data, and (3) use the model to make *predictions* on unknown data. Machine learning experience isn't required to follow this guide, but you'll need to read some Python code.\n",
