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-# Tutorials
-
-
-This section contains tutorials demonstrating how to do specific tasks
-in TensorFlow.  If you are new to TensorFlow, we recommend reading
-[Get Started with TensorFlow](/get_started/).
-
-## Images
-
-These tutorials cover different aspects of image recognition:
-
-  * @{$layers$MNIST}, which introduces convolutional neural networks (CNNs) and
-    demonstrates how to build a CNN in TensorFlow.
-  * @{$image_recognition}, which introduces the field of image recognition and
-    uses a pre-trained model (Inception) for recognizing images.
-  * @{$image_retraining}, which has a wonderfully self-explanatory title.
-  * @{$deep_cnn}, which demonstrates how to build a small CNN for recognizing
-    images.  This tutorial is aimed at advanced TensorFlow users.
-
-
-## Sequences
-
-These tutorials focus on machine learning problems dealing with sequence data.
-
-  * @{$recurrent}, which demonstrates how to use a
-    recurrent neural network to predict the next word in a sentence.
-  * @{$seq2seq}, which demonstrates how to use a
-    sequence-to-sequence model to translate text from English to French.
-  * @{$recurrent_quickdraw}
-    builds a classification model for drawings, directly from the sequence of
-    pen strokes.
-  * @{$audio_recognition}, which shows how to
-    build a basic speech recognition network.
-
-## Data representation
-
-These tutorials demonstrate various data representations that can be used in
-TensorFlow.
-
-  * @{$wide}, uses
-    @{tf.feature_column$feature columns} to feed a variety of data types
-    to linear model, to solve a classification problem.
-  * @{$wide_and_deep}, builds on the
-    above linear model tutorial, adding a deep feed-forward neural network
-    component and a DNN-compatible data representation.
-  * @{$word2vec}, which demonstrates how to
-    create an embedding for words.
-  * @{$kernel_methods},
-    which shows how to improve the quality of a linear model by using explicit
-    kernel mappings.
-
-## Non Machine Learning
-
-Although TensorFlow specializes in machine learning, the core of TensorFlow is
-a powerful numeric computation system which you can also use to solve other
-kinds of math problems.  For example:
-
-  * @{$mandelbrot}
-  * @{$pdes}