Update Readme.md

fixing links

Author: caleb-hamilton

NLDF/Readme.md

@@ -1,9 +1,7 @@
 > ## Important Information
-> This file has a lot of Latex and GitHub currently cannot render it on Markdown files. You can read all the math clearly as a [webpage](URL-PLACEHOLDER) or access this as a regular github [repository](URL-PLACEHOLDER).
+> This file has a lot of Latex and GitHub currently cannot render it on Markdown files. You can read all the math clearly as a [webpage](https://numericalalgorithmsgroup.github.io/NAGJavaExamples/NLDF/) or access this as a regular github [repository](https://github.com/caleb-hamilton/NAGJavaExamples/tree/main/NLDF).
 >
-> The source of this example can be found [here](URL-PLACEHOLDER) and the output [here](URL-PLACEHOLDER).
->
-> See the top directory of this repository for instructions to set up the [NAG Library for Java](URL-PLACEHOLDER).
+> See the top directory of this repository for instructions to set up the [NAG Library for Java](https://github.com/numericalalgorithmsgroup/NAGJavaExamples).
 
 # Loss Function and Robustness in Data-Fitting
 
@@ -337,4 +335,4 @@ So, what has happened in each case is: using $l_2$-norm loss, we move to the glo
 
 The lesson here is that the same thing that makes a loss function robust – ignoring data that lies far from the current model to some degree – can populate the search space with local minima where the model predicts some of the data well and ignores most of it. In extreme cases like arctan loss, if the starting point fits some of the data very well, the model will likely just be optimized for that portion of the data, even if it is a small portion of the whole dataset. It is therefore important to try a variety of loss functions and stating points when setting up a data-fitting problem, since these will affect both the optimal solution, as well as how easily an optimal solution is found.
 
-[Learn more about the NAG Library](https://www.nag.com/content/nag-library)
+[Learn more about the NAG Library](https://www.nag.com/content/nag-library)