diff --git a/assignment_1.ipynb b/assignment_1.ipynb
new file mode 100644
index 00000000..b47ed0cc
--- /dev/null
+++ b/assignment_1.ipynb
@@ -0,0 +1,379 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x_train shape: (50000, 32, 32, 3)\n",
+      "y_train shape: (50000, 1)\n",
+      "x_test shape: (10000, 32, 32, 3)\n",
+      "y_test shape: (10000, 1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model compiled successfully!\n",
+      "Epoch 1/15\n",
+      "1250/1250 [==============================] - 61s 48ms/step - loss: 4.1244 - accuracy: 0.0712 - val_loss: 3.5803 - val_accuracy: 0.1695\n",
+      "Epoch 2/15\n",
+      "1250/1250 [==============================] - 54s 44ms/step - loss: 3.5914 - accuracy: 0.1493 - val_loss: 3.2563 - val_accuracy: 0.2274\n",
+      "Epoch 3/15\n",
+      "1250/1250 [==============================] - 60s 48ms/step - loss: 3.3512 - accuracy: 0.1904 - val_loss: 3.0169 - val_accuracy: 0.2656\n",
+      "Epoch 4/15\n",
+      "1250/1250 [==============================] - 57s 46ms/step - loss: 3.1892 - accuracy: 0.2206 - val_loss: 2.9015 - val_accuracy: 0.2805\n",
+      "Epoch 5/15\n",
+      "1250/1250 [==============================] - 53s 43ms/step - loss: 3.0752 - accuracy: 0.2393 - val_loss: 2.8043 - val_accuracy: 0.3046\n",
+      "Epoch 6/15\n",
+      "1250/1250 [==============================] - 65s 52ms/step - loss: 2.9850 - accuracy: 0.2591 - val_loss: 2.7272 - val_accuracy: 0.3131\n",
+      "Epoch 7/15\n",
+      "1250/1250 [==============================] - 64s 51ms/step - loss: 2.9085 - accuracy: 0.2695 - val_loss: 2.7099 - val_accuracy: 0.3162\n",
+      "Epoch 8/15\n",
+      "1250/1250 [==============================] - 56s 45ms/step - loss: 2.8381 - accuracy: 0.2824 - val_loss: 2.6582 - val_accuracy: 0.3301\n",
+      "Epoch 9/15\n",
+      "1250/1250 [==============================] - 59s 47ms/step - loss: 2.7827 - accuracy: 0.2915 - val_loss: 2.6762 - val_accuracy: 0.3272\n",
+      "Epoch 10/15\n",
+      "1250/1250 [==============================] - 64s 51ms/step - loss: 2.7353 - accuracy: 0.3007 - val_loss: 2.6082 - val_accuracy: 0.3398\n",
+      "Epoch 11/15\n",
+      "1250/1250 [==============================] - 59s 47ms/step - loss: 2.6802 - accuracy: 0.3120 - val_loss: 2.6011 - val_accuracy: 0.3443\n",
+      "Epoch 12/15\n",
+      "1250/1250 [==============================] - 64s 51ms/step - loss: 2.6466 - accuracy: 0.3176 - val_loss: 2.5944 - val_accuracy: 0.3450\n",
+      "Epoch 13/15\n",
+      "1250/1250 [==============================] - 53s 43ms/step - loss: 2.6040 - accuracy: 0.3248 - val_loss: 2.6167 - val_accuracy: 0.3412\n",
+      "Epoch 14/15\n",
+      "1250/1250 [==============================] - 58s 46ms/step - loss: 2.5658 - accuracy: 0.3309 - val_loss: 2.5921 - val_accuracy: 0.3514\n",
+      "Epoch 15/15\n",
+      "1250/1250 [==============================] - 52s 42ms/step - loss: 2.5180 - accuracy: 0.3428 - val_loss: 2.5821 - val_accuracy: 0.3477\n",
+      "Training completed!\n",
+      "313/313 [==============================] - 1s 4ms/step\n",
+      "Test Accuracy: 0.3439\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.64      0.63      0.63       100\n",
+      "           1       0.40      0.53      0.46       100\n",
+      "           2       0.18      0.17      0.18       100\n",
+      "           3       0.24      0.19      0.21       100\n",
+      "           4       0.09      0.09      0.09       100\n",
+      "           5       0.21      0.22      0.22       100\n",
+      "           6       0.32      0.29      0.31       100\n",
+      "           7       0.47      0.40      0.43       100\n",
+      "           8       0.44      0.42      0.43       100\n",
+      "           9       0.46      0.46      0.46       100\n",
+      "          10       0.29      0.20      0.24       100\n",
+      "          11       0.16      0.14      0.15       100\n",
+      "          12       0.36      0.29      0.32       100\n",
+      "          13       0.28      0.42      0.34       100\n",
+      "          14       0.25      0.30      0.27       100\n",
+      "          15       0.29      0.30      0.29       100\n",
+      "          16       0.27      0.27      0.27       100\n",
+      "          17       0.63      0.44      0.52       100\n",
+      "          18       0.26      0.18      0.21       100\n",
+      "          19       0.34      0.15      0.21       100\n",
+      "          20       0.66      0.67      0.67       100\n",
+      "          21       0.40      0.51      0.45       100\n",
+      "          22       0.27      0.30      0.29       100\n",
+      "          23       0.59      0.47      0.52       100\n",
+      "          24       0.44      0.68      0.53       100\n",
+      "          25       0.38      0.24      0.29       100\n",
+      "          26       0.25      0.23      0.24       100\n",
+      "          27       0.17      0.27      0.21       100\n",
+      "          28       0.59      0.59      0.59       100\n",
+      "          29       0.31      0.29      0.30       100\n",
+      "          30       0.24      0.57      0.34       100\n",
+      "          31       0.39      0.29      0.33       100\n",
+      "          32       0.29      0.22      0.25       100\n",
+      "          33       0.54      0.29      0.38       100\n",
+      "          34       0.19      0.27      0.23       100\n",
+      "          35       0.16      0.16      0.16       100\n",
+      "          36       0.34      0.34      0.34       100\n",
+      "          37       0.20      0.27      0.23       100\n",
+      "          38       0.17      0.20      0.18       100\n",
+      "          39       0.37      0.26      0.31       100\n",
+      "          40       0.37      0.29      0.33       100\n",
+      "          41       0.60      0.59      0.59       100\n",
+      "          42       0.18      0.25      0.21       100\n",
+      "          43       0.25      0.43      0.32       100\n",
+      "          44       0.16      0.17      0.16       100\n",
+      "          45       0.15      0.16      0.15       100\n",
+      "          46       0.29      0.18      0.22       100\n",
+      "          47       0.51      0.44      0.47       100\n",
+      "          48       0.48      0.71      0.57       100\n",
+      "          49       0.46      0.42      0.44       100\n",
+      "          50       0.07      0.05      0.06       100\n",
+      "          51       0.41      0.26      0.32       100\n",
+      "          52       0.47      0.72      0.57       100\n",
+      "          53       0.54      0.60      0.57       100\n",
+      "          54       0.42      0.54      0.47       100\n",
+      "          55       0.08      0.04      0.05       100\n",
+      "          56       0.48      0.45      0.47       100\n",
+      "          57       0.33      0.33      0.33       100\n",
+      "          58       0.35      0.39      0.37       100\n",
+      "          59       0.36      0.27      0.31       100\n",
+      "          60       0.66      0.73      0.69       100\n",
+      "          61       0.52      0.50      0.51       100\n",
+      "          62       0.37      0.55      0.44       100\n",
+      "          63       0.32      0.34      0.33       100\n",
+      "          64       0.15      0.09      0.11       100\n",
+      "          65       0.22      0.08      0.12       100\n",
+      "          66       0.36      0.19      0.25       100\n",
+      "          67       0.27      0.21      0.23       100\n",
+      "          68       0.62      0.61      0.61       100\n",
+      "          69       0.43      0.60      0.50       100\n",
+      "          70       0.35      0.40      0.37       100\n",
+      "          71       0.49      0.61      0.54       100\n",
+      "          72       0.12      0.11      0.12       100\n",
+      "          73       0.35      0.27      0.31       100\n",
+      "          74       0.18      0.15      0.16       100\n",
+      "          75       0.46      0.62      0.53       100\n",
+      "          76       0.52      0.56      0.54       100\n",
+      "          77       0.14      0.12      0.13       100\n",
+      "          78       0.19      0.20      0.20       100\n",
+      "          79       0.32      0.22      0.26       100\n",
+      "          80       0.13      0.11      0.12       100\n",
+      "          81       0.36      0.27      0.31       100\n",
+      "          82       0.56      0.80      0.66       100\n",
+      "          83       0.31      0.28      0.29       100\n",
+      "          84       0.28      0.19      0.23       100\n",
+      "          85       0.44      0.42      0.43       100\n",
+      "          86       0.47      0.40      0.43       100\n",
+      "          87       0.56      0.49      0.52       100\n",
+      "          88       0.26      0.32      0.29       100\n",
+      "          89       0.35      0.42      0.38       100\n",
+      "          90       0.21      0.20      0.20       100\n",
+      "          91       0.49      0.45      0.47       100\n",
+      "          92       0.22      0.13      0.16       100\n",
+      "          93       0.19      0.17      0.18       100\n",
+      "          94       0.64      0.70      0.67       100\n",
+      "          95       0.29      0.51      0.37       100\n",
+      "          96       0.33      0.26      0.29       100\n",
+      "          97       0.20      0.33      0.25       100\n",
+      "          98       0.16      0.16      0.16       100\n",
+      "          99       0.34      0.12      0.18       100\n",
+      "\n",
+      "    accuracy                           0.34     10000\n",
+      "   macro avg       0.34      0.34      0.34     10000\n",
+      "weighted avg       0.34      0.34      0.34     10000\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "1250/1250 [==============================] - 37s 28ms/step - loss: 4.3204 - accuracy: 0.0396 - val_loss: 3.9610 - val_accuracy: 0.0913\n",
+      "Epoch 2/10\n",
+      "1250/1250 [==============================] - 56s 45ms/step - loss: 3.9097 - accuracy: 0.0952 - val_loss: 3.6293 - val_accuracy: 0.1497\n",
+      "Epoch 3/10\n",
+      "1250/1250 [==============================] - 45s 36ms/step - loss: 3.6730 - accuracy: 0.1311 - val_loss: 3.4713 - val_accuracy: 0.1763\n",
+      "Epoch 4/10\n",
+      "1250/1250 [==============================] - 67s 54ms/step - loss: 3.5062 - accuracy: 0.1600 - val_loss: 3.2421 - val_accuracy: 0.2244\n",
+      "Epoch 5/10\n",
+      "1250/1250 [==============================] - 59s 48ms/step - loss: 3.3924 - accuracy: 0.1833 - val_loss: 3.1326 - val_accuracy: 0.2420\n",
+      "Epoch 6/10\n",
+      "1250/1250 [==============================] - 56s 44ms/step - loss: 3.3015 - accuracy: 0.1978 - val_loss: 3.0528 - val_accuracy: 0.2575\n",
+      "Epoch 7/10\n",
+      "1250/1250 [==============================] - 66s 53ms/step - loss: 3.2312 - accuracy: 0.2118 - val_loss: 3.1059 - val_accuracy: 0.2481\n",
+      "Epoch 8/10\n",
+      "1250/1250 [==============================] - 60s 48ms/step - loss: 3.1750 - accuracy: 0.2235 - val_loss: 2.9237 - val_accuracy: 0.2783\n",
+      "Epoch 9/10\n",
+      "1250/1250 [==============================] - 45s 36ms/step - loss: 3.1230 - accuracy: 0.2335 - val_loss: 2.9072 - val_accuracy: 0.2857\n",
+      "Epoch 10/10\n",
+      "1250/1250 [==============================] - 66s 53ms/step - loss: 3.0866 - accuracy: 0.2400 - val_loss: 2.8651 - val_accuracy: 0.2952\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from keras.datasets import cifar100\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "import numpy as np\n",
+    "\n",
+    "# Step 1: Load the CIFAR-100 dataset\n",
+    "(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')\n",
+    "\n",
+    "# Step 2: Display dataset shape\n",
+    "print(f\"x_train shape: {x_train.shape}\")\n",
+    "print(f\"y_train shape: {y_train.shape}\")\n",
+    "print(f\"x_test shape: {x_test.shape}\")\n",
+    "print(f\"y_test shape: {y_test.shape}\")\n",
+    "\n",
+    "# Display first 5 images from the training set\n",
+    "fig, axes = plt.subplots(1, 5, figsize=(15, 5))\n",
+    "for i in range(5):\n",
+    "    axes[i].imshow(x_train[i])\n",
+    "    axes[i].set_title(f\"Class: {y_train[i][0]}\")\n",
+    "    axes[i].axis(\"off\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Step 3: Preprocess the data\n",
+    "x_train = x_train.astype('float32') / 255.0\n",
+    "x_test = x_test.astype('float32') / 255.0\n",
+    "y_train = to_categorical(y_train, 100)\n",
+    "y_test = to_categorical(y_test, 100)\n",
+    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Step 4: Build the model\n",
+    "model = Sequential([\n",
+    "    Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    Conv2D(64, (3, 3), activation='relu'),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    Flatten(),\n",
+    "    Dense(128, activation='relu'),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(100, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "# Step 5: Compile the model\n",
+    "model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "print(\"Model compiled successfully!\")\n",
+    "\n",
+    "# Step 6: Train the model\n",
+    "history = model.fit(x_train, y_train, validation_data=(x_val, y_val), batch_size=32, epochs=15, verbose=1)\n",
+    "print(\"Training completed!\")\n",
+    "\n",
+    "# Step 7: Get predictions and evaluate the model\n",
+    "y_pred = model.predict(x_test)\n",
+    "y_test_classes = np.argmax(y_test, axis=1)\n",
+    "y_pred_classes = np.argmax(y_pred, axis=1)\n",
+    "\n",
+    "test_accuracy = accuracy_score(y_test_classes, y_pred_classes)\n",
+    "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
+    "print(classification_report(y_test_classes, y_pred_classes))\n",
+    "\n",
+    "# Step 8: Plot training and validation accuracy\n",
+    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
+    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+    "plt.title('Training and Validation Accuracy')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# Step 9: Enhanced model with Dropout\n",
+    "model_enhanced = Sequential([\n",
+    "    Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    Dropout(0.25),\n",
+    "    Conv2D(64, (3, 3), activation='relu'),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    Dropout(0.25),\n",
+    "    Conv2D(128, (3, 3), activation='relu'),\n",
+    "    MaxPooling2D(pool_size=(2, 2)),\n",
+    "    Flatten(),\n",
+    "    Dense(128, activation='relu'),\n",
+    "    Dropout(0.5),\n",
+    "    Dense(100, activation='softmax')\n",
+    "])\n",
+    "\n",
+    "# Step 10: Compile and train the enhanced model\n",
+    "model_enhanced.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
+    "history_enhanced = model_enhanced.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_val, y_val))\n",
+    "\n",
+    "# Step 11: Plot enhanced model training and validation accuracy\n",
+    "plt.plot(history_enhanced.history['accuracy'], label='Training Accuracy (Enhanced)')\n",
+    "plt.plot(history_enhanced.history['val_accuracy'], label='Validation Accuracy (Enhanced)')\n",
+    "plt.title('Training and Validation Accuracy (Enhanced Model)')\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Solution for CIFAR-100 Image Classification Task\n",
+    "\n",
+    "In this solution, I performed the following steps:\n",
+    "\n",
+    "1. Loaded the CIFAR-100 dataset: This dataset consists of 100 classes with 32x32 color images. The dataset was split into training, validation, and test sets.\n",
+    "\n",
+    "\n",
+    "2. Preprocessed the data: The images were normalized to values between 0 and 1 by dividing by 255.0, and the labels were one-hot encoded to prepare them for model training.\n",
+    "\n",
+    "\n",
+    "3. Built a Convolutional Neural Network (CNN): I used a CNN with two convolutional layers, followed by max-pooling layers. A dense layer with 128 units was used, and dropout was added to prevent overfitting.\n",
+    "\n",
+    "\n",
+    "4. Trained the model: The model was trained using the Adam optimizer and categorical crossentropy loss function, with 15 epochs and a batch size of 32.\n",
+    "\n",
+    "\n",
+    "5. Evaluated the model: After training, the model was evaluated on the test set. Accuracy and other classification metrics (precision, recall, F1 score) were computed.\n",
+    "\n",
+    "\n",
+    "6. Visualized the performance: I plotted the training and validation accuracy over the epochs to understand the model’s learning behavior.\n",
+    "\n",
+    "\n",
+    "7. Enhanced the model: I added more dropout layers to improve the generalization of the model and re-trained it.\n",
+    "\n",
+    "\n",
+    "8. Visualized the enhanced model’s performance: Finally, I plotted the training and validation accuracy for the enhanced model to assess the improvements."
+   ]
+  }
+ ],
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