2.0 - MNIST Dataset.ipynb

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   "source": [
    "# MNIST Dataset"
   ]
  },
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   "source": [
    "### Also known as `digits` if you're familiar with `sklearn`:\n",
    "\n",
    "```python\n",
    "\n",
    "from sklearn.datasets import digits\n",
    "\n",
    "```"
   ]
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   "source": [
    "## Problem Definition\n",
    "\n",
    "*Recognize handwritten digits*\n",
    "![](imgs/mnist.png)"
   ]
  },
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   "source": [
    "## Data\n",
    "\n",
    "The MNIST database ([link](http://yann.lecun.com/exdb/mnist)) has a database of handwritten digits. \n",
    "\n",
    "The training set has $60,000$ samples. \n",
    "The test set has $10,000$ samples.\n",
    "\n",
    "The digits are size-normalized and centered in a fixed-size image. \n",
    "\n",
    "The data page has description on how the data was collected. It also has reports the benchmark of various algorithms on the test dataset. "
   ]
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   "source": [
    "### Load the data\n",
    "\n",
    "The data is available in the repo's `data` folder. Let's load that using the `keras` library. \n",
    "\n",
    "For now, let's load the data and see how it looks."
   ]
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  {
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   "source": [
    "import numpy as np\n",
    "import keras\n",
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Load the datasets\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Basic data analysis on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# What is the type of X_train?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "# What is the type of y_train?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Find number of observations in training data\n"
   ]
  },
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# Find number of observations in test data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Display first 2 records of X_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "# Display the first 10 records of y_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "# Find the number of observations for each digit in the y_train dataset \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "# Find the number of observations for each digit in the y_test dataset \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# What is the dimension of X_train?. What does that mean?\n",
    "\n"
   ]
  },
  {
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   "source": [
    "### Display Images\n",
    "\n",
    "Let's now display some of the images and see how they look\n",
    "\n",
    "We will be using `matplotlib` library for displaying the image"
   ]
  },
  {
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   "source": [
    "from matplotlib import pyplot\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Displaying the first training data"
   ]
  },
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   "source": [
    "fig = pyplot.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "imgplot = ax.imshow(X_train[0], cmap=mpl.cm.Greys)\n",
    "imgplot.set_interpolation('nearest')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "# Let's now display the 11th record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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