Merge pull request #5 from alhirzel/patch-2

Fix typo, "nueral"

Author: ChrisRackauckas

lecture2/ml.jmd

@@ -7,7 +7,7 @@ date: January 6th, 2020
 Let's start by discussing how to use Julia for machine learning from the
 context of scientific machine learning. The core of machine learning is the
 Universal Approximation Theroem (UAT) which states that any sufficiently nice
-function can be approximated by a sufficiently large nueral network. Since this
+function can be approximated by a sufficiently large neural network. Since this
 is what we will be looking at in practice, let's get started with training
 neural networks to match functions instead of data.
 

lecture3/diffeq_ml.jmd

@@ -223,7 +223,7 @@ augmented_data = vcat(ode_data,zeros(1,size(ode_data,2)))
 
 ### The Universal Ordinary Differential Equation
 
-This formulation of the nueral differential equation in terms of a "knowledge-embedded"
+This formulation of the neural differential equation in terms of a "knowledge-embedded"
 structure is leading. If we already knew something about the differential equation,
 could we use that information in the differential equation definition itself?
 This leads us to the idea of the universal differential equation, which is a