regression_with_keras_sequential.ipynb

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      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.datasets import boston_housing\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ]
    },
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          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
            "57344/57026 [==============================] - 0s 0us/step\n",
            "65536/57026 [==================================] - 0s 0us/step\n"
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      ],
      "source": [
        "(train_data, train_labels), (test_data, test_labels) = (boston_housing.load_data())"
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        "train_data.shape"
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        "Standardise and centralise the data - crucially avoiding data leakage i.e. we cannot subtract the mean of the test data from the test data, as that's a parameter and when it comes to predicting on real examples, we won't know the new data's mean.\n",
        "\n"
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        "mean = train_data.mean(axis = 0)\n",
        "train_data -= mean\n",
        "std = train_data.std(axis = 0)\n",
        "train_data /= std\n",
        "test_data -= mean\n",
        "test_data /= std"
      ],
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