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+# Introduction
+
+Below are few simple examples of the API to get you started with TensorFlow Learn.
+For more examples, please see [examples](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/skflow).
+
+## General tips
+
+-  It's useful to re-scale dataset before passing to estimator to 0 mean and unit standard deviation. Stochastic Gradient Descent doesn't always do the right thing when variable are very different scale.
+
+-  Categorical variables should be managed before passing input to the estimator.
+
+## Linear Classifier
+
+Simple linear classification:
+
+    from tensorflow.contrib import learn
+    from sklearn import datasets, metrics
+
+    iris = datasets.load_iris()
+    classifier = learn.TensorFlowLinearClassifier(n_classes=3)
+    classifier.fit(iris.data, iris.target)
+    score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
+    print("Accuracy: %f" % score)
+
+## Linear Regressor
+
+Simple linear regression:
+
+    from tensorflow.contrib import learn
+    from sklearn import datasets, metrics, preprocessing
+
+    boston = datasets.load_boston()
+    X = preprocessing.StandardScaler().fit_transform(boston.data)
+    regressor = learn.TensorFlowLinearRegressor()
+    regressor.fit(X, boston.target)
+    score = metrics.mean_squared_error(regressor.predict(X), boston.target)
+    print ("MSE: %f" % score)
+
+## Deep Neural Network
+
+Example of 3 layer network with 10, 20 and 10 hidden units respectively:
+
+    from tensorflow.contrib import learn
+    from sklearn import datasets, metrics
+
+    iris = datasets.load_iris()
+    classifier = learn.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
+    classifier.fit(iris.data, iris.target)
+    score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
+    print("Accuracy: %f" % score)
+
+## Custom model
+
+Example of how to pass a custom model to the TensorFlowEstimator:
+
+    from tensorflow.contrib import learn
+    from sklearn import datasets, metrics
+
+    iris = datasets.load_iris()
+
+    def my_model(X, y):
+        """This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
+        layers = learn.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
+        return learn.models.logistic_regression(layers, y)
+
+    classifier = learn.TensorFlowEstimator(model_fn=my_model, n_classes=3)
+    classifier.fit(iris.data, iris.target)
+    score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
+    print("Accuracy: %f" % score)
+
+## Saving / Restoring models
+
+Each estimator has a ``save`` method which takes folder path where all model information will be saved. For restoring you can just call ``learn.TensorFlowEstimator.restore(path)`` and it will return object of your class.
+
+Some example code:
+
+    from tensorflow.contrib import learn
+
+    classifier = learn.TensorFlowLinearRegression()
+    classifier.fit(...)
+    classifier.save('/tmp/tf_examples/my_model_1/')
+
+    new_classifier = TensorFlowEstimator.restore('/tmp/tf_examples/my_model_2')
+    new_classifier.predict(...)
+
+## Summaries
+
+To get nice visualizations and summaries you can use ``logdir`` parameter on ``fit``. It will start writing summaries for ``loss`` and histograms for variables in your model. You can also add custom summaries in your custom model function by calling ``tf.summary`` and passing Tensors to report.
+
+    classifier = learn.TensorFlowLinearRegression()
+    classifier.fit(X, y, logdir='/tmp/tf_examples/my_model_1/')
+
+Then run next command in command line:
+
+    tensorboard --logdir=/tmp/tf_examples/my_model_1
+
+and follow reported url.
+
+Graph visualization: Text classification RNN Graph image
+
+Loss visualization: Text classification RNN Loss image
+
+
+## More examples
+
+See [examples folder](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/skflow) for:
+
+-  Easy way to handle categorical variables - words are just an example of categorical variable.
+-  Text Classification - see examples for RNN, CNN on word and characters.
+-  Language modeling and text sequence to sequence.
+-  Images (CNNs) - see example for digit recognition.
+-  More & deeper - different examples showing DNNs and CNNs