diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 70bdb302..f2d7e344 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "8820fcdc5ae52ae2",
    "metadata": {
     "ExecuteTime": {
@@ -50,33 +50,58 @@
     "collapsed": false,
     "is_executing": true
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-01-26 12:04:27.706527: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from keras.datasets import cifar100\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from keras.utils import to_categorical\n",
     "\n",
     "# Load the CIFAR-100 dataset\n",
-    "(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')"
+    "(X_train, y_train), (X_test, y_test) = cifar100.load_data(label_mode='fine')\n",
+    "\n",
+    "X_train = X_train.astype('float32') / 255.0\n",
+    "X_test = X_test.astype('float32') / 255.0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a386b4072078138f",
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "indices = np.random.choice(X_train.shape[0], 9, replace=False)\n",
+    "selected_images = X_train[indices]\n",
+    "selected_labels = y_train[indices]\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 3, figsize=(8, 8))\n",
+    "\n",
+    "for i, ax in enumerate(axes.flat):\n",
+    "    ax.imshow(selected_images[i])\n",
+    "    ax.set_title(f\"{selected_labels[i]}\")\n",
+    "    ax.axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -94,14 +119,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "b18c10172fa72d0c",
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set shape: (40000, 32, 32, 3), (40000, 100)\n",
+      "Validation set shape: (10000, 32, 32, 3), (10000, 100)\n",
+      "Test set shape: (10000, 32, 32, 3), (10000, 100)\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "num_classes = 100\n",
+    "\n",
+    "y_train = to_categorical(y_train, 100)\n",
+    "y_test = to_categorical(y_test, 100)\n",
+    "\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",
+    "print(f\"Training set shape: {X_train.shape}, {y_train.shape}\")\n",
+    "print(f\"Validation set shape: {X_val.shape}, {y_val.shape}\")\n",
+    "print(f\"Test set shape: {X_test.shape}, {y_test.shape}\")"
    ]
   },
   {
@@ -119,17 +166,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "c9edafdaf887b8d5",
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " conv2d (Conv2D)             (None, 32, 32, 32)        896       \n",
+      "                                                                 \n",
+      " max_pooling2d (MaxPooling2D  (None, 16, 16, 32)       0         \n",
+      " )                                                               \n",
+      "                                                                 \n",
+      " batch_normalization (BatchN  (None, 16, 16, 32)       128       \n",
+      " ormalization)                                                   \n",
+      "                                                                 \n",
+      " conv2d_1 (Conv2D)           (None, 16, 16, 64)        18496     \n",
+      "                                                                 \n",
+      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 64)         0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " batch_normalization_1 (Batc  (None, 8, 8, 64)         256       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " conv2d_2 (Conv2D)           (None, 8, 8, 128)         73856     \n",
+      "                                                                 \n",
+      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 128)        0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " batch_normalization_2 (Batc  (None, 4, 4, 128)        512       \n",
+      " hNormalization)                                                 \n",
+      "                                                                 \n",
+      " flatten (Flatten)           (None, 2048)              0         \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 256)               524544    \n",
+      "                                                                 \n",
+      " dropout (Dropout)           (None, 256)               0         \n",
+      "                                                                 \n",
+      " dense_1 (Dense)             (None, 100)               25700     \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 644,388\n",
+      "Trainable params: 643,940\n",
+      "Non-trainable params: 448\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
    "source": [
     "from keras.models import Sequential\n",
-    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
+    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
+    "\n",
+    "# Your code here\n",
+    "\n",
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))\n",
+    "model.add(MaxPooling2D((2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(MaxPooling2D((2, 2)))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
+    "model.add(MaxPooling2D((2, 2)))\n",
+    "model.add(BatchNormalization())\n",
     "\n",
-    "# Your code here"
+    "model.add(Flatten())\n",
+    "model.add(Dense(256, activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "model.add(Dense(num_classes, activation='softmax'))\n",
+    "\n",
+    "model.summary()"
    ]
   },
   {
@@ -143,13 +260,13 @@
     "\n",
     "- Select an appropriate loss function and optimizer for your model. These can be ones we have looked at already, or they can be different. \n",
     "- Briefly explain your choices (one or two sentences each).\n",
-    "- <b>Loss function:</b> ______\n",
-    "- <b>Optimizer:</b> ______"
+    "- <b>Loss function:</b> Categorical Crossentropy is suitable for multi-class classification problems which measures the discrepancy between the predicted probability distribution and the true distribution, effectively guiding the model to output probabilities close to the true class labels.\n",
+    "- <b>Optimizer:</b> The Adam optimizer provides adaptive learning rates for each parameter. It generally results in faster convergence and improved performance on complex problems like image classification."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "ab39f4ba69d684e9",
    "metadata": {
     "collapsed": false
@@ -158,7 +275,11 @@
    "source": [
     "from keras import optimizers\n",
     "\n",
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "from keras.metrics import AUC, Precision, Recall\n",
+    "\n",
+    "model.compile(optimizer=optimizers.Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['AUC', 'accuracy'])"
    ]
   },
   {
@@ -178,14 +299,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "9de74f274ad08546",
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/15\n",
+      "1250/1250 [==============================] - 75s 58ms/step - loss: 4.2966 - auc: 0.6909 - accuracy: 0.0565 - val_loss: 3.8410 - val_auc: 0.7979 - val_accuracy: 0.1083\n",
+      "Epoch 2/15\n",
+      "1250/1250 [==============================] - 63s 50ms/step - loss: 3.7802 - auc: 0.8112 - accuracy: 0.1161 - val_loss: 3.3789 - val_auc: 0.8649 - val_accuracy: 0.1873\n",
+      "Epoch 3/15\n",
+      "1250/1250 [==============================] - 67s 54ms/step - loss: 3.4227 - auc: 0.8561 - accuracy: 0.1766 - val_loss: 3.2202 - val_auc: 0.8769 - val_accuracy: 0.2167\n",
+      "Epoch 4/15\n",
+      "1250/1250 [==============================] - 68s 55ms/step - loss: 3.1097 - auc: 0.8869 - accuracy: 0.2296 - val_loss: 2.7564 - val_auc: 0.9199 - val_accuracy: 0.3030\n",
+      "Epoch 5/15\n",
+      "1250/1250 [==============================] - 70s 56ms/step - loss: 2.8530 - auc: 0.9061 - accuracy: 0.2792 - val_loss: 2.9576 - val_auc: 0.8921 - val_accuracy: 0.2762\n",
+      "Epoch 6/15\n",
+      "1250/1250 [==============================] - 69s 55ms/step - loss: 2.6294 - auc: 0.9212 - accuracy: 0.3220 - val_loss: 2.4580 - val_auc: 0.9358 - val_accuracy: 0.3654\n",
+      "Epoch 7/15\n",
+      "1250/1250 [==============================] - 73s 59ms/step - loss: 2.4383 - auc: 0.9325 - accuracy: 0.3617 - val_loss: 2.3735 - val_auc: 0.9371 - val_accuracy: 0.3901\n",
+      "Epoch 8/15\n",
+      "1250/1250 [==============================] - 71s 57ms/step - loss: 2.2650 - auc: 0.9422 - accuracy: 0.3988 - val_loss: 2.3746 - val_auc: 0.9308 - val_accuracy: 0.3888\n",
+      "Epoch 9/15\n",
+      "1250/1250 [==============================] - 73s 58ms/step - loss: 2.1036 - auc: 0.9495 - accuracy: 0.4324 - val_loss: 2.2662 - val_auc: 0.9390 - val_accuracy: 0.4199\n",
+      "Epoch 10/15\n",
+      "1250/1250 [==============================] - 75s 60ms/step - loss: 1.9620 - auc: 0.9561 - accuracy: 0.4639 - val_loss: 2.7917 - val_auc: 0.8959 - val_accuracy: 0.3398\n",
+      "Epoch 11/15\n",
+      "1250/1250 [==============================] - 73s 58ms/step - loss: 1.8333 - auc: 0.9621 - accuracy: 0.4921 - val_loss: 2.4033 - val_auc: 0.9229 - val_accuracy: 0.3999\n",
+      "Epoch 12/15\n",
+      "1250/1250 [==============================] - 72s 58ms/step - loss: 1.7083 - auc: 0.9665 - accuracy: 0.5194 - val_loss: 2.5023 - val_auc: 0.9143 - val_accuracy: 0.3919\n",
+      "Epoch 13/15\n",
+      "1250/1250 [==============================] - 72s 57ms/step - loss: 1.6164 - auc: 0.9698 - accuracy: 0.5419 - val_loss: 2.4899 - val_auc: 0.9129 - val_accuracy: 0.4004\n",
+      "Epoch 14/15\n",
+      "1250/1250 [==============================] - 74s 59ms/step - loss: 1.5206 - auc: 0.9726 - accuracy: 0.5637 - val_loss: 2.3004 - val_auc: 0.9255 - val_accuracy: 0.4345\n",
+      "Epoch 15/15\n",
+      "1250/1250 [==============================] - 71s 57ms/step - loss: 1.4243 - auc: 0.9762 - accuracy: 0.5872 - val_loss: 2.3556 - val_auc: 0.9208 - val_accuracy: 0.4296\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "epochs = 50 # 15*3+5\n",
+    "batch_size = 32\n",
+    "\n",
+    "history = model.fit(\n",
+    "    X_train, y_train,\n",
+    "    epochs=epochs,\n",
+    "    batch_size=batch_size,\n",
+    "    validation_data=(X_val, y_val)\n",
+    ")"
    ]
   },
   {
@@ -200,16 +368,16 @@
     "- Report the accuracy of your model on the test set.\n",
     "- While accuracy is a good metric, there are many other ways to numerically evaluate a model. Report at least one other metric, and explain what it measures and how it is calculated.\n",
     "\n",
-    "- <b>Accuracy:</b> ______\n",
-    "- <b>Other metric:</b> ______\n",
-    "- <b>Reason for selection:</b> _____\n",
-    "- <b>Value of metric:</b> ______\n",
-    "- <b>Interpretation of metric value:</b> ______"
+    "- <b>Accuracy:</b> 0.4254\n",
+    "- <b>Other metric:</b> AUC\n",
+    "- <b>Reason for selection:</b> Accuracy is a straightforward and commonly used metric to evaluate the proportion of correct predictions made by the model. And, AUC provides a more nuanced view of the model's performance, especially in imbalanced datasets, by evaluating the trade-off between true positive rates and false positive rates at various threshold settings.\n",
+    "- <b>Value of metric:</b> Accuracy is typically reported as a fraction or percentage of correctly classified instances. AUC is reported as a value between 0 and 1, with higher values indicating better performance.\n",
+    "- <b>Interpretation of metric value:</b> An accuracy of 0.4254 indicates that the model correctly classified 42.54% of the test instances. An AUC of 0.9193 indicates good discriminative ability, though there is still the room for improvement.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "f670665fda92fb0e",
    "metadata": {
     "ExecuteTime": {
@@ -218,9 +386,24 @@
     },
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "313/313 [==============================] - 6s 20ms/step - loss: 2.3629 - auc: 0.9193 - accuracy: 0.4254\n",
+      "Test Accuracy: 0.4253999888896942\n",
+      "Test AUC: 0.9193165302276611\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "test_loss, test_auc, test_accuracy = model.evaluate(X_test, y_test)\n",
+    "\n",
+    "print(f'Test Accuracy: {test_accuracy}')\n",
+    "print(f'Test AUC: {test_auc}')"
    ]
   },
   {
@@ -239,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "c5b214475a496ca5",
    "metadata": {
     "ExecuteTime": {
@@ -248,9 +431,87 @@
     },
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "313/313 [==============================] - 6s 19ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "    plt.figure(figsize=(12, 5))\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
+    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title('Training and Validation Accuracy')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    plt.plot(history.history['loss'], label='Train Loss')\n",
+    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.title('Training and Validation Loss')\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
    ]
   },
   {
@@ -265,7 +526,12 @@
     "\n",
     "- Now it's time to improve your model. Implement at least one technique to improve your model's performance. You can use any of the techniques we have covered in class, or you can use a technique that we haven't covered. If you need inspiration, you can refer to the [Keras documentation](https://keras.io/).\n",
     "- Explain the technique you used and why you chose it.\n",
-    "- If you used a technique that requires tuning, explain how you selected the values for the hyperparameters."
+    "\n",
+    "Answer: I used data augmentation to improve the model's performance by applying random transformations to the training images. The techniques include rotation, width and height shifts, shear, zoom, and horizontal flips were used to introduce variability, helping the model generalize better to new data and prevent overfitting. \n",
+    "\n",
+    "- If you used a technique that requires tuning, explain how you selected the values for the hyperparameters.\n",
+    "\n",
+    "Answer: These specific hyperparameters were chosen based on common practices to balance augmentation intensity and maintain image integrity. This approach should enhance the model's robustness and accuracy on the CIFAR-100 dataset."
    ]
   },
   {
@@ -277,7 +543,21 @@
    },
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "datagen = ImageDataGenerator(\n",
+    "    rotation_range=15,\n",
+    "    width_shift_range=0.1,\n",
+    "    height_shift_range=0.1,\n",
+    "    shear_range=0.2,\n",
+    "    zoom_range=0.2,\n",
+    "    horizontal_flip=True,\n",
+    "    fill_mode='nearest'\n",
+    ")\n",
+    "\n",
+    "datagen.fit(X_train)\n"
    ]
   },
   {
@@ -296,14 +576,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "7c4701b36dc8fc55",
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "1250/1250 [==============================] - 167s 133ms/step - loss: 1.4475 - auc: 0.9706 - accuracy: 0.6011 - val_loss: 1.7696 - val_auc: 0.9500 - val_accuracy: 0.5461\n",
+      "Epoch 2/5\n",
+      "1250/1250 [==============================] - 163s 131ms/step - loss: 1.4259 - auc: 0.9713 - accuracy: 0.6059 - val_loss: 2.0009 - val_auc: 0.9362 - val_accuracy: 0.5138\n",
+      "Epoch 3/5\n",
+      "1250/1250 [==============================] - 169s 135ms/step - loss: 1.4337 - auc: 0.9708 - accuracy: 0.6030 - val_loss: 1.8830 - val_auc: 0.9427 - val_accuracy: 0.5338\n",
+      "Epoch 4/5\n",
+      "1250/1250 [==============================] - 196s 156ms/step - loss: 1.4311 - auc: 0.9704 - accuracy: 0.6043 - val_loss: 1.7610 - val_auc: 0.9504 - val_accuracy: 0.5517\n",
+      "Epoch 5/5\n",
+      "1250/1250 [==============================] - 169s 136ms/step - loss: 1.4069 - auc: 0.9708 - accuracy: 0.6092 - val_loss: 1.8145 - val_auc: 0.9475 - val_accuracy: 0.5448\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\ntest_loss, test_acc = improved_model.evaluate(X_test1, y_test)\\nprint(f'Test accuracy after improvements: {test_acc}')\\n\""
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "\n",
+    "\n",
+    "history_augmented = model.fit(datagen.flow(X_train, y_train, batch_size=32),\n",
+    "                    validation_data=(X_val, y_val),\n",
+    "                    epochs=75 # 50+20+5, \n",
+    "                    callbacks=[early_stopping])\n",
+    "\n"
    ]
   },
   {
@@ -325,6 +639,33 @@
     "# Your answer here"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "91ae1730",
+   "metadata": {},
+   "source": [
+    "Please see the answers below to the above questions.\n",
+    "\n",
+    "Model Performance:\n",
+    "The model's performance showed slight improvement in validation accuracy, reaching up to 55.17% after implementing data augmentation. This indicates a better generalization to the validation data compared to previous results without augmentation.\n",
+    "\n",
+    "Reasons for Improvement:\n",
+    "The improvement is likely due to the data augmentation techniques, which introduced variability in the training data. This helped the model learn more robust features, reducing overfitting and improving its ability to generalize to new, unseen data.\n",
+    "\n",
+    "Room for Further Improvement:\n",
+    "Yes, there is room for further improvement. The validation accuracy is still relatively low, and the validation loss suggests that the model may be struggling with certain aspects of the data.\n",
+    "\n",
+    "Future Techniques:\n",
+    "\n",
+    "Regularization - Adding dropout layers or using L2 regularization can further reduce overfitting.\n",
+    "\n",
+    "Ensembling - Combining predictions from multiple models can often yield better performance than a single model.\n",
+    "\n",
+    "Transfer Learning - Tuning up a pre-trained model like ResNet or EfficientNet on CIFAR-100 could lead to significant improvements.\n",
+    "\n",
+    "Overall, while the data augmentation has helped, exploring and implementing these additional techniques could further enhance the model's performance on the dataset."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7415f68f",
@@ -374,8 +715,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.9.19"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,