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+# Overview
+
+
+## ML for Beginners
+
+If you're new to machine learning, we recommend starting here.  You'll learn
+about a classic problem, handwritten digit classification (MNIST), and get a
+gentle introduction to multiclass classification.
+
+[View Tutorial](mnist/beginners/index.md)
+
+
+## MNIST for Pros
+
+If you're already familiar with other deep learning software packages, and are
+already familiar with MNIST, this tutorial with give you a very brief primer on
+TensorFlow.
+
+[View Tutorial](mnist/pros/index.md)
+
+
+## TensorFlow Mechanics 101
+
+This is a technical tutorial, where we walk you through the details of using
+TensorFlow infrastructure to train models at scale.  We use again MNIST as the
+example.
+
+[View Tutorial](mnist/tf/index.md)
+
+
+## Convolutional Neural Networks
+
+An introduction to convolutional neural networks using the CIFAR-10 data set.
+Convolutional neural nets are particularly tailored to images, since they
+exploit translation invariance to yield more compact and effective
+representations of visual content.
+
+[View Tutorial](deep_cnn/index.md)
+
+
+## Vector Representations of Words
+
+This tutorial motivates why it is useful to learn to represent words as vectors
+(called *word embeddings*). It introduces the word2vec model as an efficient
+method for learning embeddings. It also covers the high-level details behind
+noise-contrastive training methods (the biggest recent advance in training
+embeddings).
+
+[View Tutorial](word2vec/index.md)
+
+
+## Recurrent Neural Networks
+
+An introduction to RNNs, wherein we train an LSTM network to predict the next
+word in an English sentence.  (A task sometimes called language modeling.)
+
+[View Tutorial](recurrent/index.md)
+
+
+## Sequence-to-Sequence Models
+
+A follow on to the RNN tutorial, where we assemble a sequence-to-sequence model
+for machine translation.  You will learn to build your own English-to-French
+translator, entirely machine learned, end-to-end.
+
+[View Tutorial](seq2seq/index.md)
+
+
+## Mandelbrot Set
+
+TensorFlow can be used for computation that has nothing to do with machine
+learning.  Here's a naive implementation of Mandelbrot set visualization.
+
+[View Tutorial](mandelbrot/index.md)
+
+
+## Partial Differential Equations
+
+As another example of non-machine learning computation, we offer an example of
+a naive PDE simulation of raindrops landing on a pond.
+
+[View Tutorial](pdes/index.md)
+
+
+## MNIST Data Download
+
+Details about downloading the MNIST handwritten digits data set.  Exciting
+stuff.
+
+[View Tutorial](mnist/download/index.md)
+
+
+## Sparse Linear Regression
+
+In many practical machine learning settings we have a large number input
+features, only very few of which are active for any given example.  TensorFlow
+has great tools for learning predictive models in these settings.
+
+COMING SOON
+
+
+## Visual Object Recognition
+
+We will be releasing our state-of-the-art Inception object recognition model,
+complete and already trained.
+
+COMING SOON
+
+
+## Deep Dream Visual Hallucinations
+
+Building on the Inception recognition model, we will release a TensorFlow
+version of the [Deep Dream](https://github.com/google/deepdream) neural network
+visual hallucination software.
+
+COMING SOON
+
+
+## Automated Image Captioning
+
+TODO(vinyals): Write me, three lines max.
+
+COMING SOON
+
+
+
+<div class='sections-order' style="display: none;">
+<!--
+<!-- mnist/beginners/index.md -->
+<!-- mnist/pros/index.md -->
+<!-- mnist/tf/index.md -->
+<!-- deep_cnn/index.md -->
+<!-- word2vec/index.md -->
+<!-- recurrent/index.md -->
+<!-- seq2seq/index.md -->
+<!-- mandelbrot/index.md -->
+<!-- pdes/index.md -->
+<!-- mnist/download/index.md -->
+-->
+</div>
+
+