exercise-a-single-neuron.ipynb

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   "source": [
    "**This notebook is an exercise in the [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/ryanholbrook/a-single-neuron).**\n",
    "\n",
    "---\n"
   ]
  },
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   "source": [
    "# Introduction #\n",
    "\n",
    "In the tutorial we learned about the building blocks of neural networks: *linear units*. We saw that a model of just one linear unit will fit a linear function to a dataset (equivalent to linear regression). In this exercise, you'll build a linear model and get some practice working with models in Keras.\n",
    "\n",
    "Before you get started, run the code cell below to set everything up."
   ]
  },
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n",
      "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n",
      "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"
     ]
    }
   ],
   "source": [
    "# Setup plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('seaborn-v0_8')\n",
    "# Set Matplotlib defaults\n",
    "plt.rc('figure', autolayout=True)\n",
    "plt.rc('axes', labelweight='bold', labelsize='large',\n",
    "       titleweight='bold', titlesize=18, titlepad=10)\n",
    "\n",
    "# Setup feedback system\n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.deep_learning_intro.ex1 import *"
   ]
  },
  {
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   "source": [
    "The *Red Wine Quality* dataset consists of physiochemical measurements from about 1600 Portuguese red wines.  Also included is a quality rating for each wine from blind taste-tests. \n",
    "\n",
    "First, run the next cell to display the first few rows of this dataset."
   ]
  },
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "red_wine = pd.read_csv('../input/dl-course-data/red-wine.csv')\n",
    "red_wine.head()"
   ]
  },
  {
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   "source": [
    "You can get the number of rows and columns of a dataframe (or a Numpy array) with the `shape` attribute."
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "red_wine.shape # (rows, columns)"
   ]
  },
  {
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   "source": [
    "# 1) Input shape #\n",
    "\n",
    "How well can we predict a wine's perceived quality from the physiochemical measurements?  \n",
    "\n",
    "The target is `'quality'`, and the remaining columns are the features.  How would you set the `input_shape` parameter for a Keras model on this task?"
   ]
  },
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
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    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "input_shape = [11]\n",
    "\n",
    "# Check your answer\n",
    "q_1.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa3b50ea",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_1.hint()\n",
    "#q_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb41fd8b",
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    "tags": []
   },
   "source": [
    "# 2) Define a linear model\n",
    "\n",
    "Now define a linear model appropriate for this task. Pay attention to how many inputs and outputs the model should have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "Correct"
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   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# YOUR CODE HERE\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(units=1, input_shape=[11])\n",
    "])\n",
    "\n",
    "# Check your answer\n",
    "q_2.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "566a87b7",
   "metadata": {
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   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_2.hint()\n",
    "#q_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b11018",
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   "source": [
    "# 3) Look at the weights\n",
    "\n",
    "Internally, Keras represents the weights of a neural network with **tensors**. Tensors are basically TensorFlow's version of a Numpy array with a few differences that make them better suited to deep learning. One of the most important is that tensors are compatible with [GPU](https://www.kaggle.com/docs/efficient-gpu-usage) and [TPU](https://www.kaggle.com/docs/tpu)) accelerators. TPUs, in fact, are designed specifically for tensor computations.\n",
    "\n",
    "A model's weights are kept in its `weights` attribute as a list of tensors. Get the weights of the model you defined above. (If you want, you could display the weights with something like: `print(\"Weights\\n{}\\n\\nBias\\n{}\".format(w, b))`)."
   ]
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     "text": [
      "Weights\n",
      "<tf.Variable 'dense/kernel:0' shape=(11, 1) dtype=float32, numpy=\n",
      "array([[-0.4466681 ],\n",
      "       [ 0.45631665],\n",
      "       [ 0.6024986 ],\n",
      "       [-0.36596918],\n",
      "       [-0.37103724],\n",
      "       [ 0.07084024],\n",
      "       [-0.28283873],\n",
      "       [ 0.15279615],\n",
      "       [-0.46292043],\n",
      "       [ 0.2637961 ],\n",
      "       [-0.23988333]], dtype=float32)>\n",
      "\n",
      "Bias\n",
      "<tf.Variable 'dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> Do you see how there's one weight for each input (and a bias)? Notice though that there doesn't seem to be any pattern to the values the weights have. Before the model is trained, the weights are set to random numbers (and the bias to 0.0). A neural network learns by finding better values for its weights.\n"
      ],
      "text/plain": [
       "Correct: Do you see how there's one weight for each input (and a bias)? Notice though that there doesn't seem to be any pattern to the values the weights have. Before the model is trained, the weights are set to random numbers (and the bias to 0.0). A neural network learns by finding better values for its weights."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE\n",
    "w, b = model.weights\n",
    "print(\"Weights\\n{}\\n\\nBias\\n{}\".format(w, b))\n",
    "\n",
    "# Check your answer\n",
    "q_3.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "602e9d28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-29T22:28:39.110245Z",
     "iopub.status.busy": "2023-06-29T22:28:39.110011Z",
     "iopub.status.idle": "2023-06-29T22:28:39.121050Z",
     "shell.execute_reply": "2023-06-29T22:28:39.120178Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.018405,
     "end_time": "2023-06-29T22:28:39.122654",
     "exception": false,
     "start_time": "2023-06-29T22:28:39.104249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 2, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#3366cc\">Hint:</span> You can get the attribute of an object using the 'dot' notation: like `object.attribute`."
      ],
      "text/plain": [
       "Hint: You can get the attribute of an object using the 'dot' notation: like `object.attribute`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 2, \"questionId\": \"3_Q3\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc99\">Solution:</span> \n",
       "```python\n",
       "\n",
       "# Uncomment if you need the model from the previous question:\n",
       "# model = keras.Sequential([\n",
       "#     layers.Dense(units=1, input_shape=[11])\n",
       "# ])\n",
       "\n",
       "w, b = model.weights\n",
       "\n",
       "print(\"Weights\\n{}\\n\\nBias\\n{}\".format(w, b))\n",
       "\n",
       "```"
      ],
      "text/plain": [
       "Solution: \n",
       "```python\n",
       "\n",
       "# Uncomment if you need the model from the previous question:\n",
       "# model = keras.Sequential([\n",
       "#     layers.Dense(units=1, input_shape=[11])\n",
       "# ])\n",
       "\n",
       "w, b = model.weights\n",
       "\n",
       "print(\"Weights\\n{}\\n\\nBias\\n{}\".format(w, b))\n",
       "\n",
       "```"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "q_3.hint()\n",
    "q_3.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c547946f",
   "metadata": {
    "papermill": {
     "duration": 0.006039,
     "end_time": "2023-06-29T22:28:39.135279",
     "exception": false,
     "start_time": "2023-06-29T22:28:39.129240",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "(By the way, Keras represents weights as tensors, but also uses tensors to represent data. When you set the `input_shape` argument, you are telling Keras the dimensions of the array it should expect for each example in the training data. Setting `input_shape=[3]` would create a network accepting vectors of length 3, like `[0.2, 0.4, 0.6]`.)\n",
    " \n",
    "\n",
    "# Optional: Plot the output of an untrained linear model\n",
    " \n",
    "The kinds of problems we'll work on through Lesson 5 will be *regression* problems, where the goal is to predict some numeric target. Regression problems are like \"curve-fitting\" problems: we're trying to find a curve that best fits the data. Let's take a look at the \"curve\" produced by a linear model. (You've probably guessed that it's a line!)\n",
    " \n",
    "We mentioned that before training a model's weights are set randomly. Run the cell below a few times to see the different lines produced with a random initialization. (There's no coding for this exercise -- it's just a demonstration.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8eadddde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-29T22:28:39.147850Z",
     "iopub.status.busy": "2023-06-29T22:28:39.147511Z",
     "iopub.status.idle": "2023-06-29T22:28:39.737898Z",
     "shell.execute_reply": "2023-06-29T22:28:39.736810Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.598774,
     "end_time": "2023-06-29T22:28:39.739730",
     "exception": false,
     "start_time": "2023-06-29T22:28:39.140956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(1, input_shape=[1]),\n",
    "])\n",
    "\n",
    "x = tf.linspace(-1.0, 1.0, 100)\n",
    "y = model.predict(x)\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "plt.plot(x, y, 'k')\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.xlabel(\"Input: x\")\n",
    "plt.ylabel(\"Target y\")\n",
    "w, b = model.weights # you could also use model.get_weights() here\n",
    "plt.title(\"Weight: {:0.2f}\\nBias: {:0.2f}\".format(w[0][0], b[0]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1968c27d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-29T22:28:39.753407Z",
     "iopub.status.busy": "2023-06-29T22:28:39.753076Z",
     "iopub.status.idle": "2023-06-29T22:28:40.094745Z",
     "shell.execute_reply": "2023-06-29T22:28:40.093866Z"
    },
    "lines_to_next_cell": 0,
    "papermill": {
     "duration": 0.351067,
     "end_time": "2023-06-29T22:28:40.097126",
     "exception": false,
     "start_time": "2023-06-29T22:28:39.746059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(1, input_shape=[1]),\n",
    "])\n",
    "\n",
    "x = tf.linspace(-1.0, 1.0, 100)\n",
    "y = model.predict(x)\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "plt.plot(x, y, 'k')\n",
    "plt.xlim(-1, 1)\n",
    "plt.ylim(-1, 1)\n",
    "plt.xlabel(\"Input: x\")\n",
    "plt.ylabel(\"Target y\")\n",
    "w, b = model.weights # you could also use model.get_weights() here\n",
    "plt.title(\"Weight: {:0.2f}\\nBias: {:0.2f}\".format(w[0][0], b[0]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1b746b",
   "metadata": {
    "papermill": {
     "duration": 0.006327,
     "end_time": "2023-06-29T22:28:40.110442",
     "exception": false,
     "start_time": "2023-06-29T22:28:40.104115",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Keep Going #\n",
    "\n",
    "Add hidden layers and [**make your models deep**](https://www.kaggle.com/ryanholbrook/deep-neural-networks) in Lesson 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9766b469",
   "metadata": {
    "papermill": {
     "duration": 0.006303,
     "end_time": "2023-06-29T22:28:40.123212",
     "exception": false,
     "start_time": "2023-06-29T22:28:40.116909",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/intro-to-deep-learning/discussion) to chat with other learners.*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 20.294526,
   "end_time": "2023-06-29T22:28:43.209015",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-06-29T22:28:22.914489",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}