Copy_of_rnn.ipynb

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      "name": "Copy of rnn.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sanit4u/Cookbook/blob/master/Copy_of_rnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "# Recurrent Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "nxJfRe4bfYVA",
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      "source": [
        "## Part 1 - Data Preprocessing"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir9zwETrfbrp",
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      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQ47JAxrgmaL",
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      },
      "source": [
        "### Importing the training set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HT8_2UJegtG5",
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      },
      "source": [
        "### Feature Scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JyYgYocqhNUg",
        "colab_type": "text"
      },
      "source": [
        "### Creating a data structure with 60 timesteps and 1 output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8yaN7Zvi95l",
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      },
      "source": [
        "### Reshaping"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Part 2 - Building and Training the RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4XV88JMjHXG",
        "colab_type": "text"
      },
      "source": [
        "### Importing the Keras libraries and packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEIE-1s9jNzC",
        "colab_type": "text"
      },
      "source": [
        "### Initialising the RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62eg1OPGjT8z",
        "colab_type": "text"
      },
      "source": [
        "### Adding the first LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XBIYLyOjlMx",
        "colab_type": "text"
      },
      "source": [
        "### Adding a second LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ey3fHVnGj1cu",
        "colab_type": "text"
      },
      "source": [
        "### Adding a third LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYTrtfTmj933",
        "colab_type": "text"
      },
      "source": [
        "### Adding a fourth LSTM layer and some Dropout regularisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ABI6rOIkHhk",
        "colab_type": "text"
      },
      "source": [
        "### Adding the output layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLx4K7uUkPSh",
        "colab_type": "text"
      },
      "source": [
        "### Compiling the RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mPhwKGkkebi",
        "colab_type": "text"
      },
      "source": [
        "### Fitting the RNN to the Training set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "## Part 3 - Making the predictions and visualising the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgJO6qEDksxD",
        "colab_type": "text"
      },
      "source": [
        "### Getting the real stock price of 2017"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrvrLblxkz42",
        "colab_type": "text"
      },
      "source": [
        "### Getting the predicted stock price of 2017"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFTNs3YHk6FQ",
        "colab_type": "text"
      },
      "source": [
        "### Visualising the results"
      ]
    }
  ]
}