message: More simplifications to the text.
diff: @@ -475,20 +475,13 @@ loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  Let's take a closer look at what's happening above.
  -Our `labels` tensor contains a list of predictions for our examples, e.g. `[1,
  -9, ...]`. By using `tf.losses.sparse_softmax_cross_entropy()` we do not need to convert `labels`
  -to the corresponding
  -[one-hot encoding](https://www.quora.com/What-is-one-hot-encoding-and-when-is-it-used-in-data-science)
  -that is commonly used in machine learning applications.
  +Our `labels` tensor contains a list of prediction indices for our examples, e.g. `[1,
  +9, ...]`. `logits` contains the linear outputs of our last layer.
  -Next, we compute cross-entropy of `labels` and the softmax of the
  -predictions from our logits layer. `tf.losses.sparse_softmax_cross_entropy()` takes
  -`labels` and `logits` as arguments, performs softmax activation on
  -`logits`, calculates cross-entropy, and returns our `loss` as a scalar `Tensor`:
  +`tf.losses.sparse_softmax_cross_entropy`, calculates the softmax crossentropy
  +(aka: categorical crossentropy, negative log-likelihood) from these two inputs
  +in an efficient, numerically stable way.
  -```python
  -loss = tf.losses.sparse_softmax_cross_entropy(labels=onehot_labels, logits=logits)
  -```
  ### Configure the Training Op
  