diff --git a/doc/Jan_page.md b/doc/Jan_page.md
new file mode 100644
index 0000000..442872e
--- /dev/null
+++ b/doc/Jan_page.md
@@ -0,0 +1,54 @@
+# Jan Individual Task
+======================
+
+## Task Overview
+In addition to the overall task, I was assigned the implementation of a multi-layer perceptron model, a dataset loader for a subset of the MNIST dataset, and an accuracy metric.
+
+## Network Implementation In-Depth
+For the network part, I was tasked with making a simple MLP network model for image classification tasks. The model consists of two hidden layers with 100 neurons each followed by a leaky-relu activation. This implementation involves creating a custom class that inherits from the PyTorch `nn.Module` class. This allows our class to have two methods: the `__init__` method and a `forward` method. When we create an instance of the class, we can call the instance like a function, which will run the `forward` method.
+
+The network is initialized with the following parameters:
+* `image_shape`
+* `num_classes`
+
+The `image_shape` argument provides the shape of the input image (channels, height, width) which is used to correctly initialize the input size of the first layer. The `num_classes` argument defines the number of output neurons, corresponding to the number of classes in the dataset. 
+
+The forward method in this class processes the input as follows:
+1. Flattens the input image.
+2. Passes the flattened input through the first fully connected layer (`fc1`).
+3. Applies a LeakyReLU activation function.
+4. Passes the result through the second fully connected layer (`fc2`).
+5. Applies another LeakyReLU activation function.
+6. Passes the result through the output layer (`out`).
+
+## MNIST Dataset In-Depth
+For the dataset part, I was tasked with creating a custom dataset class for loading a subset of the MNIST dataset containing digits 0 to 3. This involved creating a class that inherits from the PyTorch `Dataset` class. 
+
+The class is initialized with the following parameters:
+* `data_path`
+* `sample_ids`
+* `train` (optional, default is False)
+* `transform` (optional, default is None)
+* `nr_channels` (optional, default is 1)
+
+The `data_path` argument stores the path to the four binary files containing MNIST dataset. The verification of presence of these files and their download, if necessary, is facilitated by the `Downloader`class. The `sample_ids` parameter contains the indices of images and their respective labels that are to be loaded from MNIST dataset. Filtering and random splitting of these indices is performed within the `load_data`function. `train`is a boolean flag indicating whether to load data from training (for training and validation splits) or from testing (test split) part of the MNIST dataset. `transform` is a callable created with `torch.compose()` to be applied on the images. `nr_channels` is not used in this dataset, only included for compatibility with other functions.
+
+The class has two main methods:
+* `__len__`: Returns the number of samples in the dataset.
+* `__getitem__`: Retrieves the image and label at the specified index.
+
+## Accuracy Metric In-Depth
+For the metric part, I was tasked with creating an accuracy metric class. The `Accuracy` class computes the accuracy of a model's predictions. The class is initialized with the following parameters:
+* `num_classes`
+* `macro_averaging` (optional, default is False)
+
+The `num_classes` argument specifies the number of classes in the classification task. The `macro_averaging`argument is a boolean flag specifying whether to compute the accuracy using micro or macro averaging.
+
+The class has the following methods:
+* `forward`: Stores the true and predicted labels computed on a batch level.
+* `_macro_acc`: Computes the macro-averaged accuracy on stored values.
+* `_micro_acc`: Computes the micro-averaged accuracy on stored values.
+* `__returnmetric__`: Returns the computed accuracy based on the averaging method for all stored predictions.
+* `__reset__`: Resets the stored true and predicted labels.
+
+The `forward` method takes the true labels and predicted labels as input and stores them. The `_macro_acc` method computes the macro-average accuracy by averaging the accuracy for each class. The `_micro_acc` method computes the micro-average accuracy by calculating the overall accuracy. The `__returnmetric__` method returns the computed accuracy based on the averaging method. The `__reset__` method resets the stored true and predicted labels to prepare for the next epoch.
\ No newline at end of file
diff --git a/doc/about.md b/doc/about.md
index 531269c..5da6b1a 100644
--- a/doc/about.md
+++ b/doc/about.md
@@ -1,3 +1,3 @@
 # About this code
 
-Work is still in progress ...
+This project was created as part of a Collaboratice Coding and Reproducible Research special curriculum, held at UiT in february 2025. 
diff --git a/doc/index.md b/doc/index.md
index 9b536b8..d525fea 100644
--- a/doc/index.md
+++ b/doc/index.md
@@ -8,12 +8,12 @@ fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in
 culpa qui officia deserunt mollit anim id est laborum.
 
 :::{toctree}
-:maxdepth: 2
-:caption: Some caption
+:maxdepth: 1
+:caption: Table of contents
 
 about.md
 Magnus_page.md
+Jan_page.md
 :::
 
-Individual Sections
-===================
+
diff --git a/pyproject.toml b/pyproject.toml
index acf6e74..a6f05a3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,7 @@ dependencies = [
     "h5py>=3.12.1",
     "isort>=6.0.0",
     "jupyterlab>=4.3.5",
+    "myst-parser>=4.0.1",
     "numpy>=2.2.2",
     "pandas>=2.2.3",
     "pip>=25.0",
diff --git a/uv.lock b/uv.lock
index 97ea764..f637107 100644
--- a/uv.lock
+++ b/uv.lock
@@ -329,6 +329,7 @@ dependencies = [
     { name = "h5py" },
     { name = "isort" },
     { name = "jupyterlab" },
+    { name = "myst-parser" },
     { name = "numpy" },
     { name = "pandas" },
     { name = "pip" },
@@ -352,6 +353,7 @@ requires-dist = [
     { name = "h5py", specifier = ">=3.12.1" },
     { name = "isort", specifier = ">=6.0.0" },
     { name = "jupyterlab", specifier = ">=4.3.5" },
+    { name = "myst-parser", specifier = ">=4.0.1" },
     { name = "numpy", specifier = ">=2.2.2" },
     { name = "pandas", specifier = ">=2.2.3" },
     { name = "pip", specifier = ">=25.0" },
@@ -992,6 +994,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899 },
 ]
 
+[[package]]
+name = "mdit-py-plugins"
+version = "0.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdown-it-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/03/a2ecab526543b152300717cf232bb4bb8605b6edb946c845016fa9c9c9fd/mdit_py_plugins-0.4.2.tar.gz", hash = "sha256:5f2cd1fdb606ddf152d37ec30e46101a60512bc0e5fa1a7002c36647b09e26b5", size = 43542 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/f7/7782a043553ee469c1ff49cfa1cdace2d6bf99a1f333cf38676b3ddf30da/mdit_py_plugins-0.4.2-py3-none-any.whl", hash = "sha256:0c673c3f889399a33b95e88d2f0d111b4447bdfea7f237dab2d488f459835636", size = 55316 },
+]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -1028,6 +1042,23 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2a/e2/5d3f6ada4297caebe1a2add3b126fe800c96f56dbe5d1988a2cbe0b267aa/mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d", size = 4695 },
 ]
 
+[[package]]
+name = "myst-parser"
+version = "4.0.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "docutils" },
+    { name = "jinja2" },
+    { name = "markdown-it-py" },
+    { name = "mdit-py-plugins" },
+    { name = "pyyaml" },
+    { name = "sphinx" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/a5/9626ba4f73555b3735ad86247a8077d4603aa8628537687c839ab08bfe44/myst_parser-4.0.1.tar.gz", hash = "sha256:5cfea715e4f3574138aecbf7d54132296bfd72bb614d31168f48c477a830a7c4", size = 93985 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5f/df/76d0321c3797b54b60fef9ec3bd6f4cfd124b9e422182156a1dd418722cf/myst_parser-4.0.1-py3-none-any.whl", hash = "sha256:9134e88959ec3b5780aedf8a99680ea242869d012e8821db3126d427edc9c95d", size = 84579 },
+]
+
 [[package]]
 name = "nbclient"
 version = "0.10.2"
@@ -1198,7 +1229,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.1.0.70"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9f/fd/713452cd72343f682b1c7b9321e23829f00b842ceaedcda96e742ea0b0b3/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f", size = 664752741 },
@@ -1209,7 +1240,7 @@ name = "nvidia-cufft-cu12"
 version = "11.2.1.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/27/94/3266821f65b92b3138631e9c8e7fe1fb513804ac934485a8d05776e1dd43/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f083fc24912aa410be21fa16d157fed2055dab1cc4b6934a0e03cba69eb242b9", size = 211459117 },
@@ -1228,9 +1259,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.6.1.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
-    { name = "nvidia-cusparse-cu12" },
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/3a/e1/5b9089a4b2a4790dfdea8b3a006052cfecff58139d5a4e34cb1a51df8d6f/nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260", size = 127936057 },
@@ -1241,7 +1272,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.3.1.170"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/db/f7/97a9ea26ed4bbbfc2d470994b8b4f338ef663be97b8f677519ac195e113d/nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1", size = 207454763 },