Merge pull request #27 from caleb-hamilton/master

Update data_fitting_robustness.ipynb

Author: caleb-hamilton

local_optimization/NLDF/data_fitting_robustness.ipynb

@@ -15,12 +15,12 @@
    "source": [
     "## Technical Setup\n",
     "\n",
-    "### NAG Library\n",
-    "To run this notebook, you will need to install the NAG Library for Python (Mark 28.5 or newer) and a license key. You can find the software and request a license key from our website here: https://www.nag.com/content/getting-started-nag-library?lang=py&os=linux\n",
+    "### NAG Library install\n",
+    "To run this notebook, you will need to install the NAG Library for Python (Mark 28.5 or newer) and a license key. You can find the software and request a license key from our website here: [Getting Started with NAG Library](https://www.nag.com/content/getting-started-nag-library?lang=py&os=linux)\n",
     "\n",
-    "### Correct Rendering of this notebook\n",
+    "### Correct rendering of this notebook\n",
     "\n",
-    "This notebook makes use of the `latex_envs` Jupyter extension for equations and references.  If the LaTeX is not rendering properly in your local installation of Jupyter, it may be because you have not installed this extension.  See at https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html for further information."
+    "This notebook makes use of the `latex_envs` Jupyter extension for equations and references.  If the LaTeX is not rendering properly in your local installation of Jupyter, it may be because you have not installed this extension.  See at [nbextensions - LATEX for Jupyter notebooks](https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/nbextensions/latex_envs/README.html) for further information."
    ]
   },
   {
@@ -538,13 +538,23 @@
     "\n",
     "## Conclusion\n",
     "\n",
-    "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."
+    "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.\n",
+    "\n",
+    "[Learn more about the NAG Library](https://www.nag.com/content/nag-library)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00b4ddf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.13 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -558,7 +568,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.7"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,