<commit_before>## tf.contrib.learn Quickstart

TensorFlow’s high-level machine learning API (tf.contrib.learn) makes it easy
to configure, train, and evaluate a variety of machine learning models. In
this quickstart tutorial, you’ll use tf.contrib.learn to construct a [neural
network](https://en.wikipedia.org/wiki/Artificial_neural_network) classifier
and train it on [Fisher’s Iris data
set](https://en.wikipedia.org/wiki/Iris_flower_data_set) to predict flower
species based on sepal/petal geometry. You’ll perform the following five
steps:

1. Load CSVs containing Iris training/test data into a TensorFlow `Dataset`
2. Construct a [neural network classifier](
../../api_docs/python/contrib.learn.html#DNNClassifier)
3. Fit the model using the training data
4. Evaluate the accuracy of the model
5. Classify new samples
<commit_msg>Update index.md<commit_after>## tf.contrib.learn 시작하기

텐서플로우의 고수준 머신러닝 API(tf.contrib.learn)는 다양한 머신러닝 모델을 쉽게 설정하고, 훈련하고, 평가할 수 있도록 해줍니다. 이 간단한 튜토리얼에서는  [신경망](https://en.wikipedia.org/wiki/Artificial_neural_network) 분류기를 만들고, [피셔의 Iris 데이터셋](https://en.wikipedia.org/wiki/Iris_flower_data_set)으로 꽃받침과 꽃잎의 정보를 이용하여 꽃의 종류를 예측할 수 있도록 분류기를 훈련시킬 것입니다. 당신은 다음의 다섯 단계를 수행할 것입니다:

1. Load CSVs containing Iris training/test data into a TensorFlow `Dataset`
2. Construct a [neural network classifier](
../../api_docs/python/contrib.learn.html#DNNClassifier)
3. Fit the model using the training data
4. Evaluate the accuracy of the model
5. Classify new samples

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