minor clean ups

docs/dataloading.md

@@ -81,7 +81,7 @@ class MyStudy(BaseDataSet):
 ```
 
 ??? note "About **kwargs in `BaseDataSet.__init__()`"
-    Find details to addtional `kwargs` in docs of `_load_data_as_full_array()` or `_load_data_as_file_paths()` in
+    Find details to additional `kwargs` in docs of `_load_data_as_full_array()` or `_load_data_as_file_paths()` in
     `xai4mri.dataloader.mri_dataloader`.
 
 `BaseDataSet` provides several attributes and methods, see details in [API reference](reference/dataloader/datasets.md)

docs/interpretation.md

@@ -7,8 +7,8 @@ and non-linear processing steps.
 These make their decision-making process hard to explain.
 
 New methods have been developed to interpret the predictions of deep learning models
-and to provide insights into their decision-making process. These methods are summerized
-under the term *explainable artificial intelligence* (XAI).
+and to provide insights into their decision-making process.
+These methods are summarized under the term *explainable artificial intelligence* (XAI).
 
 The `xai4mri` package offers a set of tools to apply XAI methods to analyze
 the predictions of deep learning models for MRI-based tasks.
@@ -81,7 +81,7 @@ for input_img, y in test_data_gen:
 ## Reverse pruning: Bringing model input and relevance maps back to the NIfTI format
 
 !!! inline end tip "Reverse pruning"
-    *Reverse pruning* is useful to compare relevance maps with other statistical maps or atlases in form of NIfTIs.
+    *Reverse pruning* is useful to compare relevance maps with other statistical maps or atlases in the form of NIfTIs.
 
 If MR images have been pruned for more efficient model training
 (see [Implement your own dataset class](dataloading.md#implement-your-own-dataset-class)),

docs/models.md

@@ -64,7 +64,7 @@ All models are `TensorFlow` / `Keras` models (`keras.Sequential`) and can be use
 ## Train a model for MRI prediction
 
 To train a model, we follow the standard procedure of `TensorFlow` / `Keras` models.
-Using the `xai4mri` data loader, you can easily load your MRI data and train the model
+Using the `xai4mri` data loader, you can load your MRI data and train the model
 (see [Create a data split for model training and evaluation](dataloading.md#create-a-data-split-for-model-training-and-evaluation)).
 
 ```python

docs/transfer.md

@@ -31,8 +31,7 @@ and apply them to new datasets with fewer samples.
 ### Analyze a dataset and a candidate model
 
 The training of deep learning models can be a time-consuming process,
-and involves a lot of trial-and-errors to find the best hyperparameters,
-and training strategies.
+and involves a lot of trial-and-errors to find the best hyperparameters.
 
 Building upon heuristics, experiences, and empirical evidence from the literature,
 there are the following goals for `xai4mri`:

mkdocs.yml

@@ -82,7 +82,7 @@ extra_css:
 - css/material.css
 - css/mkdocstrings.css
 
-# Sets the markdown extensions to use in the site.
+# Sets the Markdown extensions to use in the site.
 markdown_extensions:
 - attr_list  # Allows adding attributes to headers, paragraphs, and other elements in Markdown.
 - admonition  # Adds admonition blocks, which are like callout boxes that highlight important information.