Fixed typo and DOI formatting as instructed by Dr. Uwe Ligges

Author: bflammers

DESCRIPTION

@@ -11,8 +11,8 @@ Description: Training of neural networks for classification and regression tasks
     related plotting functions. Multiple activation functions are supported, 
     including tanh, relu, step and ramp. For the use of the step and ramp 
     activation functions in detecting anomalies using autoencoders, see 
-    Hawkins et al. (2002) <doi.org/10.1007/3-540-46145-0_17>. Furthermore, 
-    several loss functions are supporterd, including robust ones such as Huber 
+    Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, 
+    several loss functions are supported, including robust ones such as Huber 
     and pseudo-Huber loss, as well as L1 and L2 regularization. The possible 
     options for optimization algorithms are RMSprop, Adam and SGD with momentum.
     The package contains a vectorized C++ implementation that facilitates