gradient_descent.ipynb

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        "<a href=\"https://colab.research.google.com/github/AhmadAlabrash/Deep-Learning/blob/main/gradient_descent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import numpy\n",
        "import pandas\n",
        "\n",
        "def compute_cost(features, values, theta):\n",
        "    \"\"\"\n",
        "    Compute the cost of a list of parameters, theta, given a list of features \n",
        "    (input data points) and values (output data points).\n",
        "    \"\"\"\n",
        "    m = len(values)\n",
        "    sum_of_square_errors = numpy.square(numpy.dot(features, theta) - values).sum()\n",
        "    cost = sum_of_square_errors / (2*m)\n",
        "\n",
        "    return cost\n",
        "\n",
        "\n",
        "def gradient_descent(features, values, theta, alpha, num_iterations):\n",
        "    \"\"\"\n",
        "    Perform gradient descent given a data set with an arbitrary number of features.\n",
        "    \"\"\"\n",
        "\n",
        "    # Write code here that performs num_iterations updates to the elements of theta.\n",
        "    # times. Every time you compute the cost for a given list of thetas, append it \n",
        "    # to cost_history.\n",
        "    # See the Instructor notes for hints. \n",
        "    \n",
        "    cost_history = []\n",
        "    \n",
        "    for i in range(num_iterations) :\n",
        "       theta_old = theta\n",
        "       if theta > 2000 :\n",
        "         theta= theta -alpha\n",
        "       else :\n",
        "         theta= theta + alpha\n",
        "        \n",
        "       cost =compute_cost(features, values, theta)\n",
        "       cost_history.append(cost)\n",
        "       if i== 0 :\n",
        "        cost_history_old = cost_history[-1]\n",
        "       else :\n",
        "        cost_history_old = cost_history[-2]\n",
        "       \n",
        "       \n",
        "\n",
        "       if cost_history_old > cost :\n",
        "         theta = theta_old\n",
        "       else :\n",
        "         theta =theta-0 \n",
        "\n",
        "\n",
        "\n",
        "    ###########################\n",
        "    ### YOUR CODE GOES HERE ###\n",
        "    ###########################\n",
        "\n",
        "    return theta, pandas.Series(cost_history) # leave this line for the grader\n"
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        "df = pandas.read_csv('/content/baseball_stats.csv')\n",
        "\n",
        "x= df.iloc[:,4]\n",
        "\n",
        "y = df.iloc[:,5]\n",
        "\n",
        "theta =0.00008\n",
        "alpha = 0.1\n",
        "x.dtypes"
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        "compute_cost(x,y,theta)"
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        "gradient_descent(x,y,theta,alpha,2220)"
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              "            ...     \n",
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