update tutorial (#318)

examples/hyperopt/README.md

@@ -1,4 +1,4 @@
-## Hyperparameter Optimization
+# Hyperparameter Optimization
 
 This document provides a walkthrough of the hyperparameter optimization example.
 To run the application, first install this dependency.
@@ -22,7 +22,7 @@ Choosing these parameters can be challenging, and so a common practice is to
 search over the space of hyperparameters. One approach that works surprisingly
 well is to randomly sample different options.
 
-### The serial version
+## The serial version
 
 Suppose that we want to train a convolutional network, but we aren't sure how to
 choose the following hyperparameters:
@@ -74,7 +74,7 @@ Of course, as there are no dependencies between the different invocations of
 `train_cnn_and_compute_accuracy`, this computation could easily be parallelized
 over multiple cores or multiple machines. Let's do that now.
 
-### The distributed version
+## The distributed version
 
 First, let's turn `train_cnn_and_compute_accuracy` into a remote function in Ray
 by writing it as follows. In this example application, a slightly more
@@ -115,7 +115,7 @@ their values with `ray.get`.
 results = [(params, ray.get(ref)) for (params, ref) in result_refs]
 ```
 
-### Additional notes
+## Additional notes
 
 **Early Stopping:** Sometimes when running an optimization, it is clear early on
 that the hyperparameters being used are bad (for example, the loss function may

examples/lbfgs/README.md

@@ -1,4 +1,4 @@
-## Batch L-BFGS
+# Batch L-BFGS
 
 This document provides a walkthrough of the L-BFGS example. To run the
 application, first install these dependencies.
@@ -21,7 +21,7 @@ one such algorithm. It is a quasi-Newton method that uses gradient information
 to approximate the inverse Hessian of the loss function in a computationally
 efficient manner.
 
-### The serial version
+## The serial version
 
 First we load the data in batches. Here, each element in `batches` is a tuple
 whose first component is a batch of `100` images and whose second component is a
@@ -73,7 +73,7 @@ theta_init = 1e-2 * np.random.normal(size=dim)
 result = scipy.optimize.fmin_l_bfgs_b(full_loss, theta_init, fprime=full_grad)
 ```
 
-### The distributed version
+## The distributed version
 
 In this example, the computation of the gradient itself can be done in parallel
 on a number of workers or machines.