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+# Introduction
+
+Let's get you up and running with TensorFlow!
+
+But before we even get started, let's give you a sneak peak at what TensorFlow
+code looks like in the Python API, just so you have a sense of where we're
+headed.
+
+Here's a little Python program that makes up some data in three dimensions, and
+then fits a plane to it.
+
+```python
+import tensorflow as tf
+import numpy as np
+
+# Make 100 phony data points in NumPy.
+x_data = np.float32(np.random.rand(2, 100)) # Random input
+y_data = np.dot([0.100, 0.200], x_data) + 0.300
+
+# Construct a linear model.
+b = tf.Variable(tf.zeros([1]))
+W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
+y = tf.matmul(W, x_data) + b
+
+# Minimize the squared errors.
+loss = tf.reduce_mean(tf.square(y - y_data))
+optimizer = tf.train.GradientDescentOptimizer(0.5)
+train = optimizer.minimize(loss)
+
+# For initializing the variables.
+init = tf.initialize_all_variables()
+
+# Launch the graph
+sess = tf.Session()
+sess.run(init)
+
+# Fit the plane.
+for step in xrange(0, 201):
+    sess.run(train)
+    if step % 20 == 0:
+        print step, sess.run(W), sess.run(b)
+
+# Learns best fit is W: [[0.100  0.200]], b: [0.300]
+```
+
+To whet your appetite further, we suggest you check out what a classical
+machine learning problem looks like in TensorFlow.  In the land of neural
+networks the most "classic" classical problem is the MNIST handwritten digit
+classification.  We offer two introductions here, one for machine learning
+newbies, and one for pros.  If you've already trained dozens of MNIST models in
+other software packages, please take the red pill.  If you've never even heard
+of MNIST, definitely take the blue pill.  If you're somewhere in between, we
+suggest skimming blue, then red.
+
+TODO(danmane): Add in creative commons attribution for these images.
+Also, make sure the sizes are precisely the same.
+
+<div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px; display: flex; flex-direction: row">
+ <a href="../tutorials/mnist/beginners/index.md">
+   <img style="flex-grow:1; flex-shrink:1;border: 1px solid black;" src="./blue_pill.jpg">
+ </a>
+ <a href="../tutorials/mnist/pros/index.md">
+   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="./red_pill.jpg">
+ </a>
+</div>
+
+If you're already sure you want to learn and install TensorFlow you can skip
+these and charge ahead.  Don't worry, you'll still get to see MNIST -- we'll
+also use MNIST as an example in our technical tutorial where we elaborate on
+TensorFlow features.
+
+## Recommended Next Steps:
+* [Download and Setup](os_setup.md)
+* [Basic Usage](basic_usage.md)
+* [TensorFlow Mechanics 101](../tutorials/mnist/tf/index.md)
+
+
+<div class='sections-order' style="display: none;">
+<!--
+<!-- os_setup.md -->
+<!-- basic_usage.md -->
+-->
+</div>
+