This scenario is introduces the high level tf.contrib.learn API for the machine learning process. The training is performed on the MNIST dataset that is considered a *Hello world* for the deep learning examples. We will walk you through training process, evaluating the model and predicting new values using high level models called Estimators.

To take the most of this course you should know how to program in Python or other language that would allow you to understand Python syntax.

## Congratulations!

You've completed **TensorFlow Estimators for MNIST dataset** scenario.

You've learned how to:

- Read the MNIST dataset
- Create the deep neural network classifier
- Perform training and evaluate the model
- Predict new values

### Steps

### TensorFlow Estimators for MNIST dataset

#### Estimators

TensorFlow `tf.contrib.learn`

is a high level API for machine learning process. It offers variety of `Estimator`

s that represent predefined models. Some of the examples are:

- LinearClassifier - model for linear classification
- KMeansClustering - an estimator for K-Means clustering.
- DNNClassifier - a classifier for deep neural network models
- DNNRegressor - deep neural network models.

You are also provided with the techniques to write your own estimators if the list of available ones is not sufficient.

In this tutorial we will use the `DNNClassifier`

to train the model and predict the labels for the MNIST dataset. We will be solving the classification task and try to recognise the actual digit from its handwritten representation.