fix(docs) Fix typos in documentation

doc/source/tutorial-quickstart-jax.rst

@@ -226,10 +226,10 @@ set that the client might have:
 
 Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` defined above by
 means of a ``client_fn()`` callback. Note that the `context` enables you to get access
-to hyperparemeters defined in your ``pyproject.toml`` to configure the run. In this
+to hyperparameters defined in your ``pyproject.toml`` to configure the run. In this
 tutorial we access the ``local-epochs`` setting to control the number of epochs a
 ``ClientApp`` will perform when running the ``fit()`` method. You could define
-additioinal hyperparameters in ``pyproject.toml`` and access them here.
+additional hyperparameters in ``pyproject.toml`` and access them here.
 
 .. code-block:: python
 

doc/source/tutorial-quickstart-pytorch.rst

@@ -226,7 +226,7 @@ The ClientApp
 The main changes we have to make to use `PyTorch` with `Flower` will be found in the
 ``get_weights()`` and ``set_weights()`` functions. In ``get_weights()`` PyTorch model
 parameters are extracted and represented as a list of NumPy arrays. The
-``set_weights()`` function that's the oposite: given a list of NumPy arrays it applies
+``set_weights()`` function that's the opposite: given a list of NumPy arrays it applies
 them to an existing PyTorch model. Doing this in fairly easy in PyTorch.
 
 .. note::

doc/source/tutorial-quickstart-xgboost.rst

@@ -399,7 +399,7 @@ We first define a strategy for XGBoost bagging aggregation.
         return config
 
 We use two clients for this example. An ``evaluate_metrics_aggregation`` function is
-defined to collect and wighted average the AUC values from clients. The ``config_func``
+defined to collect and weighted average the AUC values from clients. The ``config_func``
 function is to return the current FL round number to client's ``fit()`` and
 ``evaluate()`` methods.