diff --git a/neural-networks-1.md b/neural-networks-1.md
index fcb63707..9dca87f8 100644
--- a/neural-networks-1.md
+++ b/neural-networks-1.md
@@ -170,7 +170,7 @@ One way to look at Neural Networks with fully-connected layers is that they defi
 
 It turns out that Neural Networks with at least one hidden layer are *universal approximators*. That is, it can be shown (e.g. see [*Approximation by Superpositions of Sigmoidal Function*](http://www.dartmouth.edu/~gvc/Cybenko_MCSS.pdf) from 1989 (pdf), or this [intuitive explanation](http://neuralnetworksanddeeplearning.com/chap4.html) from Michael Nielsen) that given any continuous function \\(f(x)\\) and some \\(\epsilon > 0\\), there exists a Neural Network \\(g(x)\\) with one hidden layer (with a reasonable choice of non-linearity, e.g. sigmoid) such that \\( \forall x, \mid f(x) - g(x) \mid < \epsilon \\). In other words, the neural network can approximate any continuous function. 
 
-If one hidden layer suffices to approximate any function, why use more layers and go deeper? The answer is that the fact that a two-layer Neural Network is a universal approximator is, while mathematically cute, a relatively weak and useless statement in practice. In one dimension, the "sum of indicator bumps" function \\(g(x) = \sum_i c_i \mathbb{1}(a_i < x < b_i)\\) where \\(a,b,c\\) are parameter vectors is also a universal approximator, but noone would suggest that we use this functional form in Machine Learning. Neural Networks work well in practice because they compactly express nice, smooth functions that fit well with the statistical properties of data we encounter in practice, and are also easy to learn using our optimization algorithms (e.g. gradient descent). Similarly, the fact that deeper networks (with multiple hidden layers) can work better than a single-hidden-layer networks is an empirical observation, despite the fact that their representational power is equal.
+If one hidden layer suffices to approximate any function, why use more layers and go deeper? The answer is that the fact that a two-layer Neural Network is a universal approximator is, while mathematically cute, a relatively weak and useless statement in practice. In one dimension, the "sum of indicator bumps" function \\(g(x) = \sum_i c_i \mathbb{1}(a_i < x < b_i)\\) where \\(a,b,c\\) are parameter vectors is also a universal approximator, but no one would suggest that we use this functional form in Machine Learning. Neural Networks work well in practice because they compactly express nice, smooth functions that fit well with the statistical properties of data we encounter in practice, and are also easy to learn using our optimization algorithms (e.g. gradient descent). Similarly, the fact that deeper networks (with multiple hidden layers) can work better than a single-hidden-layer networks is an empirical observation, despite the fact that their representational power is equal.
 
 As an aside, in practice it is often the case that 3-layer neural networks will outperform 2-layer nets, but going even deeper (4,5,6-layer) rarely helps much more. This is in stark contrast to Convolutional Networks, where depth has been found to be an extremely important component for a good recognition system (e.g. on order of 10 learnable layers). One argument for this observation is that images contain hierarchical structure (e.g. faces are made up of eyes, which are made up of edges, etc.), so several layers of processing make intuitive sense for this data domain.