Update notebooks.rst

fix typos

docs/notebooks.rst

@@ -4,7 +4,7 @@ Jupyter Notebooks
 OMLT provides Jupyter notebooks to demonstrate its core capabilities. All notebooks can be found on the OMLT 
 github `page <https://github.com/cog-imperial/OMLT/tree/main/docs/notebooks/>`_.
 
-The first set of notebooks demonstrate the basic mechanics of OMLT and show how to use it:
+The first set of notebooks demonstrates the basic mechanics of OMLT and shows how to use it:
 
 * `build_network.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/build_network.ipynb/>`_ shows how to manually create a `NetworkDefinition` object. This notebook is helpful for understanding the details of the internal layer structure that OMLT uses to represent neural networks. 
 
@@ -14,11 +14,11 @@ The first set of notebooks demonstrate the basic mechanics of OMLT and show how
 
 * `index_handling.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/index_handling.ipynb>`_ shows how to use `IndexMapper` to handle the mappings between indexes.
 
-* `bo_with_trees.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/bo_with_trees.ipynb>`_ incorporates gradient-boosted-trees into a Bayesian optimization loop to optimize the Rosenbrock function.
+* `bo_with_trees.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/bo_with_trees.ipynb>`_ incorporates gradient-boosted trees into a Bayesian optimization loop to optimize the Rosenbrock function.
 
 * `linear_tree_formulations.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/trees/linear_tree_formulations.ipynb>`_ showcases the different linear model decision tree formulations available in OMLT.
 
-The second set of notebooks give application specific examples:
+The second set of notebooks gives application-specific examples:
 
 * `mnist_example_dense.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/mnist_example_dense.ipynb>`_ trains a fully dense neural network on MNIST and uses OMLT to find adversarial examples.
 
@@ -27,4 +27,4 @@ The second set of notebooks give application specific examples:
 * `auto-thermal-reformer.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb>`_ develops a neural network surrogate (using sigmoid activations) with data from a process model built using `IDAES-PSE <https://github.com/IDAES/idaes-pse>`_.
 
 * `auto-thermal-reformer-relu.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/auto-thermal-reformer-relu.ipynb>`_ develops a neural network surrogate (using ReLU activations) with data from a process model built using `IDAES-PSE <https://github.com/IDAES/idaes-pse>`_.
-* 
+*