diff --git a/Notebooks/Notebook-3b.ipynb b/Notebooks/Notebook-3b.ipynb
index 0b895c3..ab64ef0 100644
--- a/Notebooks/Notebook-3b.ipynb
+++ b/Notebooks/Notebook-3b.ipynb
@@ -51,58 +51,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "859cbc0f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set shape: (60000, 28, 28)\n",
-      "Test set shape:     (10000, 28, 28)\n",
-      "Classes: [0 1 2 3 4 5 6 7 8 9]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 2. Load Dataset\n",
-    "# Fashion MNIST is built into Keras, downloads automatically on first run\n",
-    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
-    "\n",
-    "'''\n",
-    "import numpy as np\n",
-    "import gzip\n",
-    "import os\n",
-    "\n",
-    "def load_fashion_mnist(path):\n",
-    "    \"\"\"Load Fashion MNIST from local .gz files (Kaggle Zalando format).\"\"\"\n",
-    "    files = {\n",
-    "        'X_train': 'train-images-idx3-ubyte.gz',\n",
-    "        'y_train': 'train-labels-idx1-ubyte.gz',\n",
-    "        'X_test':  't10k-images-idx3-ubyte.gz',\n",
-    "        'y_test':  't10k-labels-idx1-ubyte.gz',\n",
-    "    }\n",
-    "\n",
-    "    with gzip.open(os.path.join(path, files['X_train'])) as f:\n",
-    "        X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n",
-    "    with gzip.open(os.path.join(path, files['y_train'])) as f:\n",
-    "        y_train = np.frombuffer(f.read(), np.uint8, offset=8)\n",
-    "    with gzip.open(os.path.join(path, files['X_test'])) as f:\n",
-    "        X_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)\n",
-    "    with gzip.open(os.path.join(path, files['y_test'])) as f:\n",
-    "        y_test = np.frombuffer(f.read(), np.uint8, offset=8)\n",
-    "\n",
-    "    return (X_train, y_train), (X_test, y_test)\n",
-    "\n",
-    "# Replace the Keras load line with:\n",
-    "(X_train, y_train), (X_test, y_test) = load_fashion_mnist('./fashion-mnist/')\n",
-    "'''\n",
-    "\n",
-    "print(\"Training set shape:\", X_train.shape)   # (60000, 28, 28)\n",
-    "print(\"Test set shape:    \", X_test.shape)    # (10000, 28, 28)\n",
-    "print(\"Classes:\", np.unique(y_train))"
-   ]
+   "outputs": [],
+   "source": "# 2. Load Dataset\n# Fashion MNIST is built into Keras — downloads automatically on first run\n(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n\n# --- Offline alternative (comment out tf.keras line above and use this instead) ---\n# import pandas as pd\n# train_df = pd.read_csv('fashion-mnist_train.csv')\n# test_df  = pd.read_csv('fashion-mnist_test.csv')\n# y_train = train_df['label'].values\n# y_test  = test_df['label'].values\n# X_train = train_df.drop('label', axis=1).values.reshape(-1, 28, 28)  # unflatten pixels to 28x28\n# X_test  = test_df.drop('label', axis=1).values.reshape(-1, 28, 28)\n\nprint(\"Training set shape:\", X_train.shape)   # (60000, 28, 28)\nprint(\"Test set shape:    \", X_test.shape)    # (10000, 28, 28)\nprint(\"Classes:\", np.unique(y_train))"
   },
   {
    "cell_type": "code",
@@ -597,4 +550,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file