diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c1fba16..8e59b31 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -17,3 +17,9 @@ repos:
       - id: end-of-file-fixer
       - id: mixed-line-ending
       - id: trailing-whitespace
+
+  - repo: https://github.com/psf/black
+    rev: 24.4.0
+    hooks:
+      - id: black
+        args: ["--line-length=120"]
diff --git a/AML_lab_1&2_task_2.ipynb b/AML_lab_1&2_task_2.ipynb
new file mode 100644
index 0000000..4049981
--- /dev/null
+++ b/AML_lab_1&2_task_2.ipynb
@@ -0,0 +1,555 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "gq8hC9S2WyIM"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch as tc\n",
+    "from torch import nn\n",
+    "from torchvision import datasets\n",
+    "from torchvision import transforms\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torch import optim\n",
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z7C9S_5YXQXU",
+    "outputId": "3ef9a131-4311-40d9-996c-97fa86562bee"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 26421880/26421880 [00:02<00:00, 11830179.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
+      "\n",
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 29515/29515 [00:00<00:00, 200436.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n",
+      "\n",
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4422102/4422102 [00:01<00:00, 3739051.57it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
+      "\n",
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
+      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 5148/5148 [00:00<00:00, 18329606.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+      "  warnings.warn(_create_warning_msg(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Transforms images to a PyTorch Tensor\n",
+    "tensor_transform = transforms.ToTensor()\n",
+    "\n",
+    "dataset = datasets.FashionMNIST(root = \"./data\",\n",
+    "\t\t\t\t\t\ttrain = True,\n",
+    "\t\t\t\t\t\tdownload = True,\n",
+    "\t\t\t\t\t\ttransform = tensor_transform)\n",
+    "\n",
+    "loader = tc.utils.data.DataLoader(dataset = dataset,\n",
+    "\t\t\t\t\t\t\t\t\tbatch_size = 32,\n",
+    "\t\t\t\t\t\t\t\t\tshuffle = True, num_workers = 3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 482
+    },
+    "id": "UG-vfDi7XR9A",
+    "outputId": "09e0948c-8318-44ec-c43b-1402a9644f27"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([32, 1, 28, 28])\n",
+      "tensor([0, 8, 3, 2, 0, 7, 2, 2, 7, 2, 8, 2, 1, 3, 6, 5, 8, 6, 7, 3, 5, 4, 0, 0,\n",
+      "        9, 0, 2, 0, 4, 9, 3, 4])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for data in loader:\n",
+    "\n",
+    "  plt.imshow(data[0][0].reshape((28, 28)))\n",
+    "  print(data[0].shape)\n",
+    "  print(data[1])\n",
+    "  break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZWDdOR_mXXZV"
+   },
+   "outputs": [],
+   "source": [
+    "from model import *\n",
+    "\n",
+    "model_linear = Model_Linear().cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iAPTG9HzYAuO"
+   },
+   "outputs": [],
+   "source": [
+    "#Loss function\n",
+    "CRITERION = nn.MSELoss()\n",
+    "\n",
+    "#Hyperparameters\n",
+    "EPOCHS = 20\n",
+    "LEARNING_RATE = 1e-3\n",
+    "WEIGHT_DECAY = 1e-8\n",
+    "\n",
+    "# Optimizers\n",
+    "OPTIMIZER = optim.Adam(model_linear.parameters(), lr=LEARNING_RATE,  weight_decay = WEIGHT_DECAY)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uiRykfL2YBHD",
+    "outputId": "92b1cb36-63ec-4e78-d391-2932b9381d4e"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r  0%|          | 0/1875 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+      "  warnings.warn(_create_warning_msg(\n",
+      "100%|██████████| 1875/1875 [00:13<00:00, 139.14it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 1, Loss: 0.0187486\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 136.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 2, Loss: 0.0139070\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:15<00:00, 122.83it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 3, Loss: 0.0128818\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 138.75it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 4, Loss: 0.0122406\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 135.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 5, Loss: 0.0117860\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 136.73it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 6, Loss: 0.0113649\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 137.75it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 7, Loss: 0.0110703\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 138.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 8, Loss: 0.0108085\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 135.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 9, Loss: 0.0105674\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 136.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 10, Loss: 0.0103270\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:15<00:00, 118.16it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 11, Loss: 0.0101989\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 136.51it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 12, Loss: 0.0100521\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:14<00:00, 131.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 13, Loss: 0.0099338\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:14<00:00, 133.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 14, Loss: 0.0098122\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 136.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 15, Loss: 0.0097602\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 137.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 16, Loss: 0.0096209\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 137.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 17, Loss: 0.0095408\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 137.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 18, Loss: 0.0094703\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 137.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 19, Loss: 0.0094416\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1875/1875 [00:13<00:00, 137.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 20, Loss: 0.0093635\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "conv_losses = []\n",
+    "conv_output = []\n",
+    "\n",
+    "for epoch in range(EPOCHS):\n",
+    "    loss_full = 0\n",
+    "    for img, label in tqdm(loader):\n",
+    "        # img = img.reshape(-1, 1, 64, 64)\n",
+    "\n",
+    "        # Forward pass\n",
+    "        img = img.to(tc.float32)\n",
+    "        img = img.cuda()\n",
+    "\n",
+    "        outputs = model_linear(img)\n",
+    "\n",
+    "        # print(img.shape, outputs.shape, decoded.shape)\n",
+    "        loss = CRITERION(outputs, img.reshape((32, 28*28)))\n",
+    "\n",
+    "        # Backward pass and optimization\n",
+    "        OPTIMIZER.zero_grad()\n",
+    "        loss.backward()\n",
+    "        OPTIMIZER.step()\n",
+    "\n",
+    "        loss_full += loss.item()\n",
+    "\n",
+    "    loss_full = loss_full/len(loader)\n",
+    "    conv_losses.append(loss_full)\n",
+    "\n",
+    "    print(f'Epoch: {epoch + 1}, Loss: {loss_full:.7f}')\n",
+    "    conv_output.append((epoch, img, outputs))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/README.md b/README.md
index 97b4080..729f8e2 100644
--- a/README.md
+++ b/README.md
@@ -253,4 +253,4 @@ study.best_params  # E.g. {'x': 2.002108042}
 ---
 ## Sources:
 
-- [github.com/leggedrobotics/plr-exercise](https://github.com/leggedrobotics/plr-exercise) by @JonasFrey96
\ No newline at end of file
+- [github.com/leggedrobotics/plr-exercise](https://github.com/leggedrobotics/plr-exercise) by @JonasFrey96
diff --git a/model.py b/model.py
new file mode 100644
index 0000000..8ae6300
--- /dev/null
+++ b/model.py
@@ -0,0 +1,27 @@
+from torch import nn
+
+
+class Model_Linear(nn.Module):
+
+    def __init__(self) -> None:
+
+        super(Model_Linear, self).__init__()
+
+        self.encoder = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(28 * 28, 128),
+            nn.BatchNorm1d(128),
+            nn.ReLU(),
+            nn.Linear(128, 60),
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(60, 128), nn.BatchNorm1d(128), nn.ReLU(), nn.Linear(128, 28 * 28), nn.Sigmoid()
+        )
+
+    def forward(self, x):
+
+        encoded = self.encoder(x)
+        decoded = self.decoder(encoded)
+
+        return decoded
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..0018422
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,651 @@
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+
+[[package]]
+name = "filelock"
+version = "3.16.1"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
+    {file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
+]
+
+[package.extras]
+docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
+typing = ["typing-extensions (>=4.12.2)"]
+
+[[package]]
+name = "fsspec"
+version = "2024.10.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
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+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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+ssh = ["paramiko"]
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+
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..3be9494
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,17 @@
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