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Fix typos in getting started section of the documentation #1399

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Fix typos in getting started section of the documentation #1399

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8 changes: 4 additions & 4 deletions docs/source/getting-started.rst
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Expand Up @@ -18,11 +18,11 @@ Otherwise, you can proceed with
``pip install pytorch-forecasting``


Alternatively, to installl the package via conda:
Alternatively, to install the package via conda:

``conda install pytorch-forecasting pytorch>=1.7 -c pytorch -c conda-forge``

PyTorch Forecasting is now installed from the conda-forge channel while PyTorch is install from the pytorch channel.
PyTorch Forecasting is now installed from the conda-forge channel while PyTorch is installed from the pytorch channel.

To use the MQF2 loss (multivariate quantile loss), also install
`pip install pytorch-forecasting[mqf2]`
Expand All @@ -45,7 +45,7 @@ The general setup for training and testing a model is
Similarly, a test dataset or later a dataset for inference can be created. You can store the dataset parameters
directly if you do not wish to load the entire training dataset at inference time.

#. Instantiate a model using the its ``.from_dataset()`` method.
#. Instantiate a model using the ``.from_dataset()`` method.
#. Create a ``lightning.Trainer()`` object.
#. Find the optimal learning rate with its ``.tuner.lr_find()`` method.
#. Train the model with early stopping on the training dataset and use the tensorboard logs
Expand All @@ -56,7 +56,7 @@ The general setup for training and testing a model is
#. Load the model from the model checkpoint and apply it to new data.


The :ref:`Tutorials <tutorials>` section provides detailled guidance and examples on how to use models and implement new ones.
The :ref:`Tutorials <tutorials>` section provides detailed guidance and examples on how to use models and implement new ones.


Example
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