Day_2_Gradient_Descent_and_Logistic_Regression.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNxBXVD9Iwf647XVyrwe2Q+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ebrarkiziloglu/ML-Notes-and-Workshops/blob/main/Day_2_Gradient_Descent_and_Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gradient Descent\n",
        "\n",
        "## Simple Gradient Descent\n",
        "\n",
        "* Start from an initial point.\n",
        "* Take the dervitaive / gradients of the cost function at each step, to determine the next step.\n",
        "* Use a step size, eta value of your choice. \n",
        "\n",
        "***!! The classical way is VERRRY small for huge data.***\n",
        "\n",
        "## Stochastic Gradient Descent (SGD)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9oIgFfYzb0CA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OP_ukUy5bwcI"
      },
      "outputs": [],
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=2)\n",
        "scaler = preprocessing.StandardScaler()\n",
        "train_X = scaler.fit_transform(train_X)\n",
        "val_X = scaler.tranform(val_X)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What if the output is binary - taking only values 0 or 1 (or multinomial):\n",
        "\n",
        "# Logistic Regression\n",
        "\n"
      ],
      "metadata": {
        "id": "l55aYh_d3aij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sigmoid Function: written by ChatGPT\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def sigmoid(X):\n",
        "    return 1 / (1 + np.exp(-X))\n",
        "\n",
        "X = np.linspace(-30, 30, 100)\n",
        "y = sigmoid(X)\n",
        "\n",
        "plt.plot(X, y)\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('sigmoid(x)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "buzlYP6849VR",
        "outputId": "7fb061c7-aab3-4830-e434-c4944f68f387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the sigmoid function in the gradient calculations, for Logistic Regression:\n",
        "def gradient_w(w, b, X, y):\n",
        "  return X.T.dot(sigmoid(w.dot(X.T) + b) - y)"
      ],
      "metadata": {
        "id": "YjXP8FCK5TOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "2zTYUyZaAwRl"
      }
    }
  ]
}