add new deep dream stuff from aws repo

TensorFlow/intro_linear_model.py

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+import numpy as np
+import tensorflow as tf
+
+# Model linear regression y = Wx + b
+x = tf.placeholder(tf.float32, [None, 1])
+W = tf.Variable(tf.zeros([1,1]))
+b = tf.Variable(tf.zeros([1]))
+product = tf.matmul(x,W)
+y = product + b
+y_ = tf.placeholder(tf.float32, [None, 1])
+
+# Cost function sum((y_-y)**2)
+cost = tf.reduce_mean(tf.square(y_-y))
+
+# Training using Gradient Descent to minimize cost
+train_step = tf.train.GradientDescentOptimizer(0.0000001).minimize(cost)
+
+sess = tf.Session()
+init = tf.initialize_all_variables()
+sess.run(init)
+steps = 1000
+for i in range(steps):
+
+    # Create fake data for y = W.x + b where W = 2, b = 0
+    xs = np.array([[i]])
+    ys = np.array([[2*i]])
+
+    # Train
+    feed = { x: xs, y_: ys }
+    sess.run(train_step, feed_dict=feed)
+
+    print("After %d iteration:" % i)
+    print("W: %f" % sess.run(W))
+    print("b: %f" % sess.run(b))
+
+    print("cost: %f" % sess.run(cost, feed_dict=feed))