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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-06-15 09:37:49 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-06-15 09:41:52 -0700 |
| commit | ff29c995ea187932a4f6f25c0a7a1a5c4577655e (patch) | |
| tree | 453b4cfe9a177aecceb4a2af65bb6b155b6db2a7 /tensorflow/contrib/kernel_methods | |
| parent | 6138e267a2cb00a242cc1badf226b8e5cda1da48 (diff) | |
Updates get_started/tflearn.md to use tf.estimator api, renames the file and updates links.
PiperOrigin-RevId: 159114013
Diffstat (limited to 'tensorflow/contrib/kernel_methods')
| -rw-r--r-- | tensorflow/contrib/kernel_methods/g3doc/tutorial.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/tensorflow/contrib/kernel_methods/g3doc/tutorial.md b/tensorflow/contrib/kernel_methods/g3doc/tutorial.md index 9877375c2c..f39a8d80d2 100644 --- a/tensorflow/contrib/kernel_methods/g3doc/tutorial.md +++ b/tensorflow/contrib/kernel_methods/g3doc/tutorial.md @@ -13,7 +13,7 @@ for sparse features is in the works. We will use [tf.contrib.learn](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn) (TensorFlow's high-level Machine Learning API) Estimators for our ML models. The tf.contrib.learn API reduces the boilerplate code one needs to write for configuring, training and evaluating models and will let us focus on the core -ideas. If you are not familiar with this API, [tf.contrib.learn Quickstart](https://www.tensorflow.org/get_started/tflearn) is a good place to start. We +ideas. If you are not familiar with this API, [tf.estimator Quickstart](https://www.tensorflow.org/get_started/estimator) is a good place to start. We will use MNIST, a widely-used dataset containing images of handwritten digits (between 0 and 9). The tutorial consists of the following steps: |