Incorrect Conversion from md&ipynb

[LinkHS]

1. symbol.md

The contents in symbol.md (see attachment symbol.md) is like below. When I run "notedown symbol.md --to markdown --strip" (see chapter 1 and attachment symbol.md), then it becomes like chapter 2.

---

ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
print('number of outputs = %d\nthe first output = \n%s' % (
            len(ex), ex[0].asnumpy()))

For neural nets, a more commonly used pattern is simple_bind, which
creates all of the argument arrays for you. Then you can call forward,
and backward (if the gradient is needed) to get the gradient.

Load and Save

Logically symbols correspond to ndarrays. They both represent a tensor. They both
are inputs/outputs of operators. We can either serialize a Symbol object by
using pickle, or by using save and load methods directly as we discussed in
NDArray tutorial.

---

2. symbol_result.md

---

ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3]))
print('number of outputs = %d\nthe first output = \n%s' % (
            len(ex), ex[0].asnumpy()))

For neural nets, a more commonly used pattern is simple_bind, which
creates all of the argument arrays for you. Then you can call forward,
and
backward (if the gradient is needed) to get the gradient.

2. Load and

Save

Logically symbols correspond to ndarrays. They both represent a tensor.
They both
are inputs/outputs of operators. We can either serialize a Symbol
object by
using pickle, or by using save and load methods directly as we
discussed in
[NDArray tutorial](http://mxnet.io/tutorials/basic/ndarray.html
#serialize-from-to-distributed-filesystems).

---

3. Attachment

symbol.zip